Look Ahead of Links/Alter Links

ABSTRACT

A computationally-implemented method comprises retrieving at least a portion of data from a data source, determining an acceptability of an effect of the retrieved at least a portion of the data at least in part via a virtual machine representation of at least a part of an end user&#39;s real machine having one or more end-user specified preferences, and providing at least one data display option to the end user&#39;s real machine based on the determining acceptability of the effect of the retrieved at least a portion of the data.

CROSS-REFERENCE TO RELATED APPLICATIONS

The present application is related to and claims the benefit of theearliest available effective filing date(s) from the following listedapplication(s) (the “Related Applications”) (e.g., claims earliestavailable priority dates for other than provisional patent applicationsor claims benefits under 35 USC §119(e) for provisional patentapplications, for any and all parent, grandparent, great-grandparent,etc. applications of the Related Application(s)).

RELATED APPLICATIONS

-   1. For purposes of the USPTO extra-statutory requirements, the    present application constitutes a continuation-in-part of United    States Patent Application entitled LOOK AHEAD OF LINKS/ALTER LINKS,    naming Gary W. Flake William H. Gates, III; Roderick A. Hyde;    Edward K. Y. Jung; Royce A. Levien; Robert W. Lord; Mark A. Malamud;    Richard F. Rashid; John D. Rinaldo, Jr.; Clarence T. Tegreene;    Charles Whitmer; and Lowell L. Wood, Jr. as inventors, filed 21 Dec.    2007, application Ser. No. 12/005,064, which is currently    co-pending, or is an application of which a currently co-pending    application is entitled to the benefit of the filing date.-   2. For purposes of the USPTO extra-statutory requirements, the    present application constitutes a continuation-in-part of United    States Patent Application entitled LOOK AHEAD OF LINKS/ALTER LINKS,    naming Gary W. Flake William H. Gates, III; Roderick A. Hyde;    Edward K. Y. Jung; Royce A. Levien; Robert W. Lord; Mark A. Malamud;    Richard F. Rashid; John D. Rinaldo, Jr.; Clarence T. Tegreene;    Charles Whitmer; and Lowell L. Wood, Jr. as inventors, filed Dec.    27, 2007, application Ser. No. 12/005,637, which is currently    co-pending, or is an application of which a currently co-pending    application is entitled to the benefit of the filing date.-   3. For purposes of the USPTO extra-statutory requirements, the    present application constitutes a continuation-in-part of United    States Patent Application entitled LOOK AHEAD OF LINKS/ALTER LINKS,    naming Gary W. Flake William H. Gates, III; Roderick A. Hyde;    Edward K. Y. Jung; Royce A. Levien; Robert W. Lord; Mark A. Malamud;    Richard F. Rashid; John D. Rinaldo, Jr.; Clarence T. Tegreene;    Charles Whitmer; and Lowell L. Wood, Jr. as inventors, filed Mar. 6,    2008, application Ser. No. 12/074,855, which is currently    co-pending, or is an application of which a currently co-pending    application is entitled to the benefit of the filing date.

The United States Patent Office (USPTO) has published a notice to theeffect that the USPTO's computer programs require that patent applicantsreference both a serial number and indicate whether an application is acontinuation or continuation-in-part. Stephen G. Kunin, Benefit ofPrior-Filed Application, USPTO Official Gazette Mar. 18, 2003, availableat http://www.uspto.gov/web/offices/com/sol/og/2003/week11/patbene.htm.The present Applicant has provided above a specific reference to theapplication(s) from which priority is being claimed as recited bystatute. Applicant understands that the statute is unambiguous in itsspecific reference language and does not require either a serial numberor any characterization, such as “continuation” or“continuation-in-part,” for claiming priority to U.S. patentapplications. Notwithstanding the foregoing, Applicant understands thatthe USPTO's computer programs have certain data entry requirements, andhence Applicant is designating the present application as acontinuation-in-part of its parent applications as set forth above, butexpressly points out that such designations are not to be construed inany way as any type of commentary and/or admission as to whether or notthe present application contains any new matter in addition to thematter of its parent application(s).

All subject matter of the Related Application and of any and all parent,grandparent, great-grandparent, etc. applications of the RelatedApplications is incorporated herein by reference to the extent suchsubject matter is not inconsistent herewith.

BACKGROUND

Web sites often contain links to other web sites enabling a user tonavigate from one web site to another. Certain links may contain datathat may compromise security and/or privacy. Certain links may containdata that a user may not desire to view.

SUMMARY

A computationally implemented method includes, but is not limited to:retrieving at least a portion of data from a data source; determining anacceptability of an effect of the retrieved at least a portion of thedata at least in part via a virtual machine representation of at least apart of an end user's real machine having one or more end-user specifiedpreferences; and providing at least one data display option to the enduser's real machine based on the determining acceptability of the effectof the retrieved at least a portion of the data. In addition to theforegoing, other computationally implemented method aspects aredescribed in the claims, drawings, and text forming a part of thepresent disclosure.

In one or more various aspects, related systems include but are notlimited to circuitry and/or programming for effecting theherein-referenced method aspects; the circuitry and/or programming canbe virtually any combination of hardware, software, and/or firmwareconfigured to effect the herein-referenced method aspects depending uponthe design choices of the system designer.

A computationally implemented system includes, but is not limited to:means for retrieving at least a portion of data from a data source;means for determining an acceptability of an effect of the retrieved atleast a portion of the data at least in part via a virtual machinerepresentation of at least a part of an end user's real machine havingone or more end-user specified preferences; and means for providing atleast one data display option to the end user's real machine based onthe determining acceptability of the effect of the retrieved at least aportion of the data. In addition to the foregoing, other system aspectsare described in the claims, drawings, and text forming a part of thepresent disclosure.

A computationally implemented system includes, but is not limited to:circuitry for retrieving at least a portion of data from a data source;circuitry for determining an acceptability of an effect of the retrievedat least a portion of the data at least in part via a virtual machinerepresentation of at least a part of an end user's real machine havingone or more end-user specified preferences; and circuitry for providingat least one data display option to the end user's real machine based onthe determining acceptability of the effect of the retrieved at least aportion of the data. In addition to the foregoing, other system aspectsare described in the claims, drawings, and text forming a part of thepresent disclosure.

The foregoing summary is illustrative only and is not intended to be inany way limiting. In addition to the illustrative aspects, embodiments,and features described above, further aspects, embodiments, and featureswill become apparent by reference to the drawings and the followingdetailed description.

BRIEF DESCRIPTION OF THE FIGURES

FIG. 1A illustrates an exemplary environment in which one or moretechnologies may be implemented.

FIG. 1B illustrates an operational view of a real machine in which atleast a portion of the system illustrated in FIG. 1A has beenimplemented.

FIG. 1C illustrates an operational view of a real machine in which atleast a portion of the system illustrated in FIG. 1A has beenimplemented.

FIG. 1D illustrates an operational view of a real machine in which atleast a portion of the system illustrated in FIG. 1A has beenimplemented.

FIG. 2 illustrates an operational flow representing example operationsrelated to providing acceptable data to a real machine.

FIG. 3 illustrates an alternative embodiment of the operational flow ofFIG. 2.

FIG. 4 illustrates an alternative embodiment of the operational flow ofFIG. 2.

FIG. 5 illustrates an alternative embodiment of the operational flow ofFIG. 2.

FIG. 6 illustrates an alternative embodiment of the operational flow ofFIG. 2.

FIG. 7 illustrates an alternative embodiment of the operational flow ofFIG. 2.

FIG. 8 illustrates an alternative embodiment of the operational flow ofFIG. 2.

FIG. 9 illustrates an alternative embodiment of the operational flow ofFIG. 2.

FIG. 10 illustrates an alternative embodiment of the operational flow ofFIG. 2.

FIG. 11 illustrates an alternative embodiment of the operational flow ofFIG. 2.

FIG. 12 illustrates an alternative embodiment of the operational flow ofFIG. 2.

FIG. 13 illustrates an alternative embodiment of the operational flow ofFIG. 2.

FIG. 14 illustrates an alternative embodiment of the operational flow ofFIG. 2.

FIG. 15 illustrates an alternative embodiment of the operational flow ofFIG. 2.

FIG. 16 illustrates an alternative embodiment of the operational flow ofFIG. 2.

FIG. 17 illustrates an alternative embodiment of the operational flow ofFIG. 2.

FIG. 18 illustrates an alternative embodiment of the operational flow ofFIG. 2.

FIG. 19 illustrates an alternative embodiment of the operational flow ofFIG. 2.

FIG. 20 illustrates an alternative embodiment of the operational flow ofFIG. 2.

FIG. 21 illustrates an alternative embodiment of the operational flow ofFIG. 2.

FIG. 22 illustrates an alternative embodiment of the operational flow ofFIG. 2.

FIG. 23 illustrates an alternative embodiment of the operational flow ofFIG. 2.

FIG. 24 illustrates an alternative embodiment of the operational flow ofFIG. 2.

FIG. 25 illustrates an alternative embodiment of the operational flow ofFIG. 2.

FIG. 26 illustrates an alternative embodiment of the operational flow ofFIG. 2.

FIG. 27 illustrates an alternative embodiment of the operational flow ofFIG. 2.

FIG. 28 illustrates an alternative embodiment of the operational flow ofFIG. 2.

FIG. 29 illustrates an alternative embodiment of the operational flow ofFIG. 2.

FIG. 30 illustrates an alternative embodiment of the operational flow ofFIG. 2.

FIG. 31 illustrates an alternative embodiment of the operational flow ofFIG. 2.

FIG. 32 illustrates an alternative embodiment of the operational flow ofFIG. 2.

FIG. 33 illustrates an alternative embodiment of the operational flow ofFIG. 2.

FIG. 34 illustrates an alternative embodiment of the operational flow ofFIG. 2.

FIG. 35 illustrates an alternative embodiment of the operational flow ofFIG. 2.

FIG. 36 illustrates an alternative embodiment of the operational flow ofFIG. 2.

FIG. 37 illustrates an alternative embodiment of the operational flow ofFIG. 2.

DETAILED DESCRIPTION

In the following detailed description, reference is made to theaccompanying drawings, which form a part hereof. In the drawings,similar symbols typically identify similar components, unless contextdictates otherwise. The illustrative embodiments described in thedetailed description, drawings, and claims are not meant to be limiting.Other embodiments may be utilized, and other changes may be made,without departing from the spirit or scope of the subject matterpresented here.

Referring to FIG. 1A, a system 100 related to looking ahead for data isillustrated. The system 100 may include a data retriever engine 102, anEffect of data acceptability determination engine 106, and a dataprovider engine 108. Effect of data acceptability determination engine106 may include a virtual machine module 118 including one or morevirtual machines 11, 12, and/or 13, further including a databaseexamination engine 112, a data traverser engine 114, and a local dataexamination engine 116, and a user preference database 120. Dataprovider engine 108 may include a data modification engine 122 that mayfurther include a data obfuscation engine 124 and a data anonymizationengine 126. Data provider engine 108 may also include a data redirectionengine 128.

FIG. 1B shows an operational view of real machine 130 (e.g., a desktop,notebook, or other type computing system, including or excluding one ormore peripheral devices) in which at least a portion of system 100 (FIG.1A) has been implemented. In some instances, system 100 is at leastpartially implemented in a single core processor at least partiallyresident within real machine 130 (e.g., one or more virtual machines ofvirtual machine module 118 at least partially implemented on asingle-core processor of real machine 130). In other instances, system100 is at least partially implemented in a multi-core processor at leastpartially resident within real machine 130 (e.g., one or more virtualmachines of virtual machine module 118 at least partially respectivelyimplemented on one or more cores of a multi-core processor of realmachine 130). In other instances, system 100 is at least partiallyimplemented in a single-core processor at least partially non-residentwithin real machine 130 (e.g., one or more virtual machines of virtualmachine module 118 at least partially implemented on a single-coreprocessor of a hosting site/machine/system physically distal from realmachine 130). In other instances, system 100 is at least partiallyimplemented in a multi-core processor at least partially non-residentwithin real machine 130 (e.g., one or more virtual machines of virtualmachine module 118 at least partially respectively implemented on one ormore cores of a multi-core processor of a hosting site/machine/systemphysically distal from real machine 130).

FIG. 1B depicts real machine 130 containing data 110 (e.g., a Web page)containing Link 1, Link 2, and Link 3. FIG. 1B illustrates an example inwhich at least a part of system 100 traverses Link 1, Link 2, and Link 3of data 110 via virtual machine representations of real machine 130. Insome instances, such virtual machine traversals are utilized toprospectively determine what might happen should real machine 130 beused to traverse such links. For example, determining how suchtraversal(s) might compare to one or more user-associated preferences ofreal machine 130 (e.g., that user 10 prefers to visit sites havingcontent acceptable to a defined organization, such a government; thatuser 10 prefers not to visit sites having malware or spyware; that user10 prefers not to visit sites that reset real machine hardware options(e.g., audio/visual peripherals); that user 10 prefers not to visitsites that reset real machine software options (e.g., proxy servers);etc.). User-associated preferences of real machine 130 may be stored inuser preference database 120 (FIG. 1A) of Effect of data acceptabilitydetermination engine 106 (FIG. 1A). User preference database 120 maycontain user preferences with respect to content of the real machine130, hardware of the real machine 130, software of the real machine 130and an operating system of the real machine 130. User preferencedatabase 120 may be in communication with virtual machine module 118(FIG. 1A). Specifically, virtual machine module 118 (FIG. 1A) mayreceive user preference database information from user preferencedatabase 120 (FIG. 1A) and spawn a copy of at least a portion of userpreference database 120 (FIG. 1A) on each of virtual machines 11, 12,and/or 13.

FIG. 1B further illustrates virtual machine 11. Virtual machine 11 maybe illustrated as included in virtual machine module 118 (FIG. 1A) ofEffect of data acceptability determination engine 106 (FIG. 1A). FIG. 1Bshows virtual machine 11 encompassing a virtual machine representationof real machine 130, post (e.g. subsequent to) activation of Link 1(e.g., as at least a part of real machine 130 would exist had link 1actually been traversed on real machine 130). FIG. 1B depicts virtualmachine 11 including a virtual machine representation of the content ofthe real machine 130 post activation of Link 1. Examples of such contentinclude a movie file, a music file, a script (e.g., Java script orActive X control), a markup language, an email, etc., downloaded ontoreal machine 130 from one or more sources associated withactivation/traversal of Link 1.

FIG. 1B also illustrates virtual machine 11 including a virtual machinerepresentation of software (e.g., a state of software) of the realmachine 130 post (e.g. subsequent to) activation of Link 1. Examples ofsuch software might include a commercial word processing program orsuite of programs (e.g. Microsoft® Office for Windows), an open sourceWeb browser (e.g., Mozilla's Firefox® Browser), an AJAX mash up (e.g.,an executing JavaScript™ and/or data retrieved by same via an XML-likescheme), or a commercial database management system (e.g., one or moreof Oracle Corporation's various products), a commercialanti-malware/spyware programs (e.g., such as those of SymantecCorporation or McAfee, Inc.), etc.

FIG. 1B also illustrates virtual machine 11 including a virtual machinerepresentation of hardware (e.g. a state of the hardware) of the realmachine 130 post activation of Link 1. Examples of such hardware mightinclude all or part of a chipset (e.g., data processor and/or graphicsprocessor chipsets such as those of Intel Corporation and/orNvidiaCorporation), a memory chip (e.g., flash memory and/or randomaccess memories such as those of Sandisk Corporation and/or SamsungElectronics, Co., LTD), a data bus, a hard disk (e.g., such as those ofSeagate Technology, LLC), a network adapter (e.g., wireless and/or wiredLAN adapters such as those of Linksys and/or CiscoTechnology, Inc.),printer, a removable drive (e.g., flash drive), a cell phone, etc.

FIG. 1B also illustrates virtual machine 11 including a virtual machinerepresentation of an operating system (e.g., a state of an operatingsystem and/or network operating system) of the real machine 130 postactivation of Link 1. Examples of such an operating system might includea computer operating system (e.g., e.g. Microsoft® Windows 2000, Unix,Linux, etc) and/or a network operating system (e.g., the InternetOperating System available from Cisco Technology, Inc. Netware®available from Novell, Inc., and/or Solaris available from SunMicrosystems, Inc.).

FIG. 1B also illustrates that virtual machine 11 may run on core 11 of amulti-core processor. In addition to the herein, those skilled in theart will appreciate that the virtual machine representations discussedherein are not limited to specific examples described, but insteadinclude any components of real machine 130 as such might be understoodin the art. Examples of the foregoing would include firmware, logicassociated with display units, logic associated with robotics,application specific integrated circuits, etc.

As noted, in some instances system 100 may traverse (e.g. view) links ofdata 110 via one or virtual machine representations of at least a partof real machine 130. Accordingly, FIG. 1B shows virtual machine 12encompassing a virtual machine representation of real machine 130 (e.g.,one or more states of one or more components associated with realmachine 130), post activation of Link 2. FIG. 1B depicts virtual machine12 at least partly running on core 12 of a multi-core processor. Virtualmachine module 118 (FIG. 1A) of Effect of data acceptabilitydetermination engine 106 (FIG. 1A) may be illustrated to include virtualmachine 12.

FIG. 1B also illustrates virtual machine 12 may include a virtualmachine representation of content (e.g. a video) of real machine 130post activation of Link 2, a virtual machine representation of software(e.g. Microsoft Office for Windows) of real machine 130 post activationof Link 2, a virtual machine representation of hardware (e.g. thecircuitry or processor of the real machine) of real machine 130 postactivation of Link 2, and a virtual machine representation of operatingsystem (e.g. Microsoft Windows 2000, XP, Vista) of real machine 130 postactivation of Link 2.

As noted, in some instances system 100 may traverse links of data 110via one or more virtual machine representations of at least a part ofreal machine 130. Accordingly, FIG. 1B shows virtual machine 13encompassing a virtual machine representation of real machine 130, postactivation of Link 3 (e.g., representative of one or more states of oneor more hardware/software/firmware components of/resident within realmachine 130). The foregoing constitutes one example of how system 100may use virtual machine 13 to traverse Link 3 (e.g. download a mediaplayer as requested by activation of Link 3). Virtual machine module 118(FIG. 1A) of Effect of data acceptability determination engine 106 (FIG.1A) may include virtual machine 13. FIG. 1B further illustrates virtualmachine 13 may include a virtual machine representation of the content(e.g. a markup language) of the real machine 130 post activation of Link3, a virtual machine representation of the software (e.g. Unix) of thereal machine 130 post activation of Link 3, a virtual machinerepresentation of the hardware (e.g. a hard disk) of the real machine130 post activation of Link 3, and a virtual machine representation ofthe operating system (e.g. Solaris Operating System) of the real machine130 post activation of Link 3. FIG. 1B shows that virtual machine 13 maybe run on core 13 of a multi-core processor.

Upon traversal of links 1, 2, and 3 by virtual machines 11, 12, and/or13, respectively, each of virtual machines 11, 12, and/or 13 maydetermine whether an effect of the retrieved data is acceptable to auser based on a user's preferences. Virtual machines 11, 12, and/or 13may compare the traversed data to one or more user preferences stored ina user preference database 120 (FIG. 1A). User preference databaseinformation may be communicated to virtual machine module 118 (FIG. 1A)and a copy of at least a portion of user preference database 120 may bespawned (e.g. generated) on each of virtual machines 11, 12, and/or 13.Virtual machines 11, 12, and/or 13 may communicate the results of arespective comparison of activation of a link (e.g. loading at least aportion of a link's content onto a virtual machine 11, 12, and/or 13) toa user preference (e.g. a preference not to load malware onto a user'sreal machine) to virtual machine module 118 (FIG. 1A). Virtual machinemodule 118 (FIG. 1A) may communicate the results of a comparison ofactivation of a link to a user preference to Effect of dataacceptability determination engine 106 (FIG. 1A). Effect of dataacceptability determination engine 106 may communicate the comparison tothe data provider engine 108 (FIG. 1A). The data provider engine 108 maythen provide the results (e.g. one or more weblinks approved forviewing) to a real machine 130 (e.g. a computing device with or withoutassociated peripherals) that may be viewable to a user 10 on a display.

FIG. 1C shows a partial follow-on operational view of real machine 130(e.g. a desktop, notebook, or other type computing system of an enduser) in which at least a portion of system 100 (FIG. 1A) has beenimplemented (e.g., a follow-on operational view of the systems/methodsillustrated as in FIG. 1B). Specifically, FIG. 1C shows a drill-downview of an example of the virtual machine 11 including a virtual machinerepresentation of the content of the real machine 130 post activation ofLink 1 (e.g., a drill-down on the systems/methods shown/described inrelation to FIG. 1B). In this drill down example, depicted is thevirtual machine representation of the content of the real machine 130post activation of Link 1. In the example shown, the content is depictedas data 110 having Link 4, Link 5 and Link 6. As a specific example,data 110 could be a Web page containing embedded Link 4 to anadvertisement, Link 5 to a video file, and Link 6 to a still image file.

In some instances, system 100 may use additional virtual machinerepresentations of at least a part of real machine 130 to prospectivelytraverse Link 4, Link 5, and Link 6. Accordingly, FIG. 1C illustratessystem 100 generating virtual machine representations of real machine130, used to traverse Links 4, 5, and 6, in the context of virtualmachines 21, 22, and 23, respectively. Those skilled in the art willthus appreciate that, in the example shown in FIG. 1C, system 100 iscreating second-order virtual machine representations to prospectivelyinvestigate the effects on the states of various components of realmachine 130 via sequential traversals of links. That is, the virtualmachine representations of real machine 130 encompassed in virtualmachine 21, virtual machine 22, and virtual machine 23 of FIG. 1C aregenerated by system 100 based on the first-order virtual machinerepresentation of virtual machine 11 as shown/described in relation toFIG. 1B.

Upon traversal of links 4, 5, and 6 by virtual machines 21, 22, and 23,respectively, each of virtual machines 21, 22, and 23 may determinewhether an effect of the retrieved data is acceptable to a user based ona user's preferences. Virtual machines 21, 22, and 23 may compare thetraversed data to one or more user preferences stored in a userpreference database 120 (FIG. 1A). As previously described, userpreference database information may be communicated to virtual machinemodule 118 (FIG. 1A), and a copy of at least a portion of userpreference database 120 may be spawned (e.g. generated) on each ofvirtual machines 11, 12, and/or 13. Virtual machine 11 may thencommunicate user preference database information (e.g., a preference toprevent installation of shareware onto an end user's real machine) toeach of virtual machines 21, 22, and 23, and a copy of a user preferencedatabase 120 (FIG. 1A) may be spawned on each of virtual machines 21,22, and 23. Virtual machines 21, 22, and 23 may communicate the resultsof a respective comparison of an activation of a link (e.g. loading atleast a portion of a link's content onto a virtual machine 21, 22,and/or 23) to a user preference (e.g. a preference to preventinstallation of a rootkit onto a user's real machine) to virtual machine11. Virtual machine 11 may communicate the results of a comparison tovirtual machine module 118 (FIG. 1A). Virtual machine module 118 (FIG.1A) may communicate the results of a comparison of an activation of alink to a user preference to effect of data acceptability determinationengine 106 (FIG. 1A). Effect of data acceptability determination engine106 may communicate the comparison to the data provider engine 108 (FIG.1A). The data provider engine 108 may then provide the results (e.g. oneor more weblinks approved for viewing) to a real machine 130 (e.g. anend user's computing device with or without associated peripherals) thatmay be viewable to a user 10 on a display.

FIG. 1C shows virtual machine 21 encompassing a virtual machinerepresentation of real machine 130 post (e.g. subsequent to) asequential activation of Link 1 (e.g., as shown on FIG. 1B) then Link 4(e.g., as shown on FIG. 1C). FIG. 1C depicts that in one instancevirtual machine 21 may be run on core 31 of a multi-core processor. FIG.1C depicts system 100 traversing Link 4 via a virtual machinerepresentation of real machine 130 encompassed within virtual machine21. Accordingly, FIG. 1C illustrates virtual machine 21 including avirtual machine representation of content (e.g., a movie, web page,music file, etc.) of the real machine 130 post sequential activation ofLink 1 then Link 4, a virtual machine representation of the software(e.g.) of the real machine 130 post sequential activation of Link 1 thenLink 4, a virtual machine representation of the hardware (e.g. thecircuitry or processor of the real machine) of the real machine 130 postsequential activation of Link 1 then Link 4, and a virtual machinerepresentation of the operating system (e.g. Linux) of the real machine130 post sequential activation of Link 1 then Link 4.

FIG. 1C shows virtual machine 22 encompassing a virtual machinerepresentation of real machine 130 post (e.g., subsequent to) asequential activation of Link 1 (e.g., as shown on FIG. 1B) then Link 5(e.g., as shown on FIG. 1C). FIG. 1C depicts that in one instancevirtual machine 22 may be run on core 32 of a multi-core processor. FIG.1C depicts system 100 traversing Link 5 via a virtual machinerepresentation of real machine 130 encompassed within virtual machine22. Accordingly, FIG. 1C illustrates virtual machine 22 including avirtual machine representation of content (e.g. a graphical image, atext file, an email, etc) of the real machine 130 post (e.g., subsequentto) sequential activation of Link 1 then Link 5, a virtual machinerepresentation of software (e.g. an AJAX mashup) of the real machine 130post sequential activation of Link 1 then Link 5, a virtual machinerepresentation of hardware (e.g. a network adapter) of the real machine130 post sequential activation of Link 1 then Link 5, and a virtualmachine representation of an operating system (e.g. Mac OS/X) of thereal machine 130 post sequential activation of Link 1 then Link 5.

FIG. 1C shows virtual machine 23 may be a virtual machine representationof real machine 130 post (e.g., subsequent to) sequential activation ofLink 1 (e.g., FIG. 1B) then Link 6 (e.g., FIG. 1C). FIG. 1C depicts thatin one instance virtual machine 23 may be run on core 33 of a multi-coreprocessor. System 100 is shown using virtual machine 23 to traverse Link6.

FIG. 1C further illustrates virtual machine 23 encompassing a virtualmachine representation of the content (e.g. a music file) of the realmachine 130 post sequential activation of Link 1 then Link 6, a virtualmachine representation of the software (e.g. a commercial databasemanagement system) of the real machine 130 post sequential activation ofLink 1 then Link 6, a virtual machine representation of the hardware(e.g. a removable drive) of the real machine 130 post sequentialactivation of Link 1 then Link 6, and a virtual machine representationof the operating system (e.g. GNU, Berkeley Software Distribution) ofthe real machine 130 post sequential activation of Link 1 then Link 6(e.g., as such might appear after activation of a link installed by arootkit via malware/spyware).

Those skilled in the art will appreciate that system 100 may generate asmany virtual machines as necessary to traverse individual links ofinterest, and that the examples herein are used for sake of clarity.Those skilled in the art will appreciate that examples used herein aremeant to be indicative of the fact that system 100 can run in whole orin part on proximate single or multi-core machines and/or distal singleor multi-core machines, on distributed computing systems (e.g., GRID orclustered), on local computing systems, or hosted computing systems,etc.

FIG. 1D shows a representative view of an implementation of real machine130 (e.g., a desktop, notebook, or other type computing system, and/orone or more peripheral devices). FIG. 1D illustrates thatimplementations of real machine 130 may include all/part of computingdevice 132 and/or all/part of one or one or more peripherals associatedcomputing device 132. The computing device 132 may be any device capableof processing one or more programming instructions. For example, thecomputing device 132 may be a desktop computer, a laptop computer, anotebook computer, a mobile phone, a personal digital assistant (PDA),combinations thereof, and/or other suitable computing devices.

As noted, in some instances, real machine 130 may also include at leasta portion of one or more peripheral devices connected/connectable (e.g.,via wired, waveguide, or wireless connections) to real machine 130.Peripheral devices may include one or more printers 134, one or more faxmachines 136, one or more peripheral memory devices 138 (e.g., flashdrive, memory stick), one or more network adapters 139 (e.g., wired orwireless network adapters), one or more music players 140, one or morecellular telephones 142, one or more data acquisition devices 144 (e.g.robots) and/or one or more device actuators 146 (e.g., an hydraulic arm,a radiation emitter, or any other component(s) of industrial/medicalsystems).

FIG. 2 illustrates an operational flow 200 representing exampleoperations related to FIGS. 1A, 1B, 1C and 1D. In FIG. 2 and infollowing figures that include various examples of operational flows,discussion and explanation may be provided with respect to theabove-described examples of FIGS. 1A, 1B, 1C, and 1D and/or with respectto other examples and contexts. However, it should be understood thatthe operational flows may be executed in a number of other environmentsand contexts, and/or in modified versions of FIGS. 1A, 1B, IC, and ID.Also, although the various operational flows are presented in thesequence(s) illustrated, it should be understood that the variousoperations may be performed in other orders than those which areillustrated, or may be performed concurrently.

After a start operation, the operational flow 200 moves to an operation210. Operation 210 depicts retrieving at least a portion of data from adata source (e.g. a computer accessible from the internet). For example,FIG. 1A shows a data retriever engine 102. Data retriever engine 102 mayretrieve (e.g. download) data 110 (e.g. a web page) from a data sourcesuch as a computer accessible from the internet. For example, dataretriever engine 102 may set a URL and add a querystring value to theURL. Data retriever engine 102 may then make a request to the URL andscan the response received from the URL. Data 110 may be a web site orweb page containing one or more links to additional web sites, such asshown, for example, in FIG. 1B and/or FIG. 1C. Data 110 may in someinstances be textual, a two-dimensional or three-dimensional image,audible, or video representations, which in some instances may entailprogramming code such as html, javascript, C, C++, or any otherprogramming code capable of producing text, visual images, audiocontent, video content or any combination of text, visual images,audible content and video content.

Then, operation 220 depicts determining an acceptability of an effect ofthe retrieved at least a portion of the data at least in part via avirtual machine representation of at least a part of an end user's realmachine having one or more end-user specified preferences. Continuingthe example above, FIG. 1A illustrates an Effect of data acceptabilitydetermination engine 106. Effect of data acceptability determinationengine 106 may receive data from data retriever engine 102. Effect ofdata acceptability determination engine 106 (FIG. 1A) may utilize, forexample, virtual machine 12 (FIG. 1A) spawned by virtual machine module118 to determine whether data associated with Link 2 would result in achange in the operating system of real machine 130 contra to a userpreference regarding the operating system as stored in the userpreference database 120.

Then, operation 230 depicts providing at least one data display optionto the end user's real machine based on the determining acceptability ofthe effect of the retrieved at least a portion of the data. FIG. 1Aillustrates a data provider engine 108. Data provider engine 108 may bein communication with Effect of data acceptability determination engine106, which may receive data from data retriever engine 102. Effect ofdata acceptability determination engine 106 may transfer at least aneffect of data acceptability determination to the data provider engine108 to provide at least one data display option. In one example, dataprovider engine 108 (FIG. 1A) provides data via placing the data on avisual display, where the data is such that it meets one or morethresholds associated with the effect of data acceptabilitydetermination. Provided data may be a list of web links, a web page, orother data (e.g., text, video, audio) that either have been deemedacceptable by Effect of data acceptability determination engine 106 orthat have been modified (e.g., obfuscated), such as by data modificationengine 122, such that the to-be-displayed data is determined to beacceptable under user preferences. Display option may include providinga visual display of the data (e.g., displaying text, playing a video,etc.), providing an audible presentation of the data (e.g., playing anaudio file), providing a mixed media display of the data (e.g., playinga video and an associated audio file), and so on. Provided data may bemodified via the data modification engine 122. For instance, provideddata may be obfuscated via the data obfuscation engine 124 (e.g., atleast a portion of the displayed data may be blurred out or disabled),or provided data may be anonymized via the data anonymization engine 126(e.g., at least a portion of the data may be deleted entirely). Dataprovider engine 108 (FIG. 1A) may receive at least one displayinstruction (e.g. OK to display links 1 and 2) from at least onecomponent of Effect of data acceptability determination engine 106 (FIG.1A) for at least a portion of data to be displayed. For instance, eachof virtual machines 11, 12, and/or 13 may include one or moreinstruction generating modules configured to provide an instruction tothe Effect of data acceptability determination engine 106 after acomparison of an activation of a link to a user preference stored in acopy of the user preference database 120 (FIG. 1A) spawned on thevirtual machines 11, 12, and/or 13. Such instruction may include aninstruction to the data provider engine 108 to prevent the data providerengine 108 from displaying data that may configure a hardware profile ofreal machine 130 counter to anti-viral settings stored in the userpreference database 120 (FIG. 1A), or an instruction to the dataprovider engine 108 to prevent the data provider engine 108 fromdisplaying data that may configure an operating system of real machine130 counter to a previous operating system of the real machine (130)(e.g. determine if a rootkit has been installed).

FIG. 3 illustrates alternative embodiments of the example operationalflow 200 of FIG. 2. FIG. 3 illustrates example embodiments where theoperation 220 may include at least one additional operation. Additionaloperations may include an operation 302, an operation 304, and/or anoperation 306.

The operation 302 illustrates determining an acceptability of an effectof the retrieved at least a portion of the data at least in part via avirtual machine representation of at least a part of an end user's realmachine having one or more end-user specified preferences by examining adatabase of known data for data information. Continuing the exampleabove, data retriever engine 102 (FIG. 1A) may retrieve data 110retrieved from a data source by the data retriever engine 102 andcommunicate data 110 to Effect of data acceptability engine 106, whichtransfers data 110 to the database examination engine 112. Databaseexamination engine 112 may be configured to examine a database of dataprovided, for example, by a data provider service or a database of datastored on a real machine 130 and compare examined database data to theretrieved data 110. Effect of data acceptability determination engine106 may utilize format/protocol information to determine whetherdatabase examination engine should call a specific database or library(e.g., a Windows Media Player library) to obtain file information. Fileinformation may be utilized to compare retrieved data 110 to data storedin a library. For instance, a database may include a list of linksviewed by a user or pre-approved by a user based on one or moreuser-specified preferences, such as links from a specific source ofinformation (e.g., the Roman Catholic Church) and may provide anindication to the Effect of data acceptability engine 106 that data 110is pre-approved data.

The operation 304 illustrates determining an acceptability of an effectof the retrieved at least a portion of the data at least in part via avirtual machine representation of at least a part of an end user's realmachine having one or more end-user specified preferences by traversingdata in real time. Continuing the example above, data retriever engine102 (FIG. 1A) may retrieve data 110 retrieved from a data source by thedata retriever engine 102 and communicate data 110 to Effect of dataacceptability engine 106, which transfers data 110 to the data traverserengine 114. Data traverser engine 114 may traverse links of data 110.For instance, data traverser engine 114 may be configured to traverse(e.g., scan) the data 110 to determine the content of the data (e.g.,text, images, video files). Accordingly, FIG. 1B shows virtual machine11 encompassing a virtual machine representation of real machine 130,post activation of Link 3 (e.g., representative of one or more states ofone or more hardware/software/firmware components of/resident withinreal machine 130). Data traversal may occur in real time (e.g.,simultaneously as data is loading). Upon traversal of at least a portionof Link 3 by the data traverser engine 114, Effect of data acceptabilitydetermination engine 106 may determine whether an effect of theretrieved data is acceptable to a user based on a user's preferences bycomparing the traversed data to one or more user preferences stored in auser preference database 120 (FIG. 1A).

The operation 306 illustrates determining an acceptability of an effectof the retrieved at least a portion of the data at least in part via avirtual machine representation of at least a part of an end user's realmachine having one or more end-user specified preferences by locallyexamining data. For instance, continuing the example above, Effect ofdata acceptability engine 106 may receive data 110 retrieved from a datasource (e.g. a computer accessible through the internet) by the dataretriever engine 102 and communicate data 110 to a local dataexamination engine 116 (FIG. 1A) of virtual machine 11. Local dataexamination engine 116 may extract data content information from atleast a portion of the data. Local data examination engine 116 maylocally (e.g., on the real machine 130) examine (e.g., analyze) at leasta portion of the data (e.g., one or more pointers in the data) todetermine data content (e.g., an audio file is a .wav file). Forinstance, local data examination engine 116 may view an amount of htmlsource code to locate markers signifying the format of at least aportion of data content. The local examination engine 116 may examinethe data 110 on the real machine 130 at the location of the real machine130 (e.g. executed on a subsystem within an end user's real machine) todetermine data content (e.g. a downloadable software program).

FIG. 4 illustrates alternative embodiments of the example operationalflow 200 of FIG. 2. FIG. 4 illustrates example embodiments where theoperation 220 may include at least one additional operation. Additionaloperations may include an operation 402, an operation 404, and/or anoperation 406.

The operation 402 illustrates determining an acceptability of an effectof the retrieved at least a portion of the data at least in part via avirtual machine representation of at least a part of an end user's realmachine having one or more end-user specified preferences by examiningdata to locate references to additional data. Continuing the exampleabove, FIG. 1A shows the Effect of data acceptability determinationengine 106. Effect of data acceptability determination engine 106 mayreceive data from data retriever engine 102. FIG. 1A further illustratesthe Effect of data acceptability determination engine 106 furtherincluding a virtual machine module 118 and a user preference database120. Effect of data acceptability determination engine 106 may transferthe data to the virtual machine module 118. Virtual machine module 118(FIG. 1A) may spawn at least one virtual machine 11, 12, and/or 13, andtransfer the data to at least one of virtual machines 11, 12, and/or 13further including data traverser engine 114 and local data examinationengine 116. At least one of virtual machines 11, 12, and/or 13 mayutilize at least one of data traverser engine 114 and local dataexamination engine 116 to examine (e.g. scan) at least a portion of data(e.g. an imbedded link on a webpage) to determine if the data referencesadditional data (e.g. one or more additional links). Additional data maybe a web page comprising text and/or an image, a link to a web page, avideo or any combination of text, images, links to web pages, or videos.Virtual machines 11, 12, and/or 13 may traverse additional data todetermine an acceptability of an effect of the data. Effect of dataacceptability determination may be communicated to Effect of dataacceptability determination engine 106 that may communicate an effect ofdata acceptability determination to a data provider engine 108.

The operation 404 illustrates determining an acceptability of an effectof the retrieved at least a portion of the data at least in part via avirtual machine representation of at least a part of an end user's realmachine having one or more end-user specified preferences by determiningwhether data references additional data when loading. Continuing theexample above, FIG. 1A illustrates the Effect of data acceptabilitydetermination engine 106 further including a virtual machine module 118and a user preference database 120. Effect of data acceptabilitydetermination engine 106 may transfer retrieved data to the virtualmachine module 118. Virtual machine module 118 (FIG. 1A) may spawn atleast one virtual machine 11, 12, and/or 13, and transfer the data to atleast one of virtual machines 11, 12, and/or 13 further including datatraverser engine 114 and local data examination engine 116. At least oneof virtual machines 11, 12, and/or 13 may utilize at least one of datatraverser engine 114 and local data examination engine 116 to examineretrieved data in real time as it loads. For instance, if a link to awebpage immediately (e.g. as soon as the link is activated) referencesan additional link (e.g. to redirect a user), a virtual machine 11, 12,and/or 13 may determine that such a reference to an additional link(e.g., a pop-up, selectable URL) has been made. Virtual machines 11, 12,and/or 13 may determine whether data references additional data at anytime when the data is loading. Effect of data acceptabilitydetermination engine 106 may communicate an effect of data acceptabilitydetermination to a data provider engine 108.

The operation 406 illustrates determining an acceptability of an effectof the retrieved at least a portion of the data at least in part via avirtual machine representation of at least a part of an end user's realmachine having one or more end-user specified preferences by issuing arequest to a remote computer for additional data information. Continuingthe example above, FIG. 1A illustrates the Effect of data acceptabilitydetermination engine 106 further including a virtual machine module 118and a user preference database 120. Effect of data acceptabilitydetermination engine 106 may transfer retrieved data to the virtualmachine module 118. Virtual machine module 118 (FIG. 1A) may spawn atleast one virtual machine 11, 12, and/or 13 and transfer the data to atleast one of virtual machines 11, 12, and/or 13 further including datatraverser engine 114 and local data examination engine 116. At least oneof virtual machines 11, 12, and/or 13 may utilize at least one of datatraverser engine 114 and local data examination engine 116 to examinedata of an additional link or links and issue a request to receiveadditional data information from the remote computer or remote system(e.g. a computer at a geographically distinct location). System 100 mayinclude any number of communication modules (not shown) configured tocommunicate over local or remote communication channels to the remoteserver or remote system.

FIG. 5 illustrates alternative embodiments of the example operationalflow 200 of FIG. 2. FIG. 5 illustrates example embodiments where theoperation 220 may include at least one additional operation. Additionaloperations may include an operation 502, an operation 504, and/or anoperation 506.

The operation 502 illustrates determining an acceptability of an effectof the retrieved at least a portion of the data at least in part via avirtual machine representation of at least a part of an end user's realmachine having one or more end-user specified preferences by examining acopy of data from a location geographically distinct from a location ofthe retrieved at least a portion of the data. Continuing the exampleabove, Effect of data acceptability determination engine 106 maytransfer retrieved data to the virtual machine module 118. FIG. 1Aillustrates the Effect of data acceptability determination engine 106further including a virtual machine module 118 and a user preferencedatabase 120. Virtual machine module 118 (FIG. 1A) may spawn at leastone virtual machine 11, 12, and/or 13 and transfer the data to at leastone of virtual machines 11, 12, and/or 13 to issue a request to a remotecomputer to examine additional data information at the remote location(e.g. a remote server farm). System 100 may include any number ofcommunication modules (not shown) configured to communicate over localor remote communication channels to the remote server or remote system.

The operation 504 illustrates generating a substantial duplicate of atleast a part of an end user's real machine at a location geographicallydistinct from a location of the retrieved at least a portion of thedata. Continuing the example above, a virtual machine 11, 12, and/or 13of the real machine 130 may be located at a geographically distinctlocation such as a remote server, or a remote system configuredduplicate data from the real machine 130 and to receive and examine realmachine information transferred to the remote server or remote system.In one embodiment, generating a substantial duplicate of at least a partof an end user's real machine at a location geographically distinct froma location of the retrieved at least a portion of the data may include aremote server or remote system gathering parameters of an end user'sreal machine to assist in generating a virtual duplicate of the enduser's real machine at the remote server or remote system (e.g. hostedon, running on, or being implemented on the remote server or remotesystem).

FIG. 6 illustrates alternative embodiments of the example operationalflow 200 of FIG. 2. FIG. 6 illustrates example embodiments where theoperation 220 may include at least one additional operation. Additionaloperations may include an operation 602, an operation 604, and/or anoperation 606.

The operation 602 illustrates determining an acceptability of an effectof the retrieved at least a portion of the data at least in part via avirtual machine representation of a substantial portion of an end user'sreal machine having one or more end-user specified preferences.Continuing the example above, FIG. 1B illustrates virtual machines 11,12, and 13 including a virtual machine representation of content of thereal machine 130, software of the real machine 130, hardware of the realmachine 130, and an operating system of the real machine 130. Virtualmachines 11, 12, and/or 13 may include most or all of at least one ofthe content of the real machine 130 (e.g. a substantial portion of thetext, image, audio, and video files of the real machine), software ofthe real machine 130 (e.g. a substantial portion of any program or suiteof programs installed on the real machine), hardware of the real machine130 (a substantial portion of the circuitry comprising the realmachine), and/or an operating system of the real machine 130 (e.g. asubstantial portion of a Windows® operating system installed on the realmachine).

The operation 604 illustrates determining an acceptability of an effectof data at least in part via a virtual machine representation operatingat a location of the retrieved at least a portion of the data.Continuing the example above, Effect of data acceptability determinationengine 106 may transfer retrieved data to the virtual machine module118. FIG. 1A illustrates the Effect of data acceptability determinationengine 106 further including a virtual machine module 118 and a userpreference database 120. Virtual machine module 118 includes virtualmachines 11, 12, and/or 13. FIG. 1A further illustrates the Effect ofdata acceptability determination engine 106 further including a virtualmachine module 118 and a user preference database 120. Virtual machinemodule 118 (FIG. 1A) may spawn at least one virtual machine 11, 12,and/or 13 and transfer the data to at least one of virtual machines 11,12, and/or 13. In one implementation, all or part of virtual machines11, 12, and/or 13 may be generated on the real machine 130 (e.g. as asubsystem of real machine 130). For instance, all or part of virtualmachines 11, 12, and/or 13 may be generated on a disk, a memory chip, acore of a multi-core processor, etc. of an end user's real machine.

FIG. 7 illustrates alternative embodiments of the example operationalflow 200 of FIG. 2. FIG. 7 illustrates example embodiments where theoperation 220 may include at least one additional operation. Additionaloperations may include an operation 702, an operation 704, an operation706, and/or an operation 708.

The operation 702 illustrates determining an acceptability of an effectof the retrieved at least a portion of the data on at least two virtualmachine representations of at least a part of an end user's real machinehaving one or more end-user specified preferences. Continuing theexample above, FIG. 1A shows the Effect of data acceptabilitydetermination engine 106. Effect of data acceptability determinationengine 106 may receive data from data retriever engine 102. FIG. 1Afurther illustrates the Effect of data acceptability determinationengine 106 further including a virtual machine module 118 and a userpreference database 120. Effect of data acceptability determinationengine 106 may transfer the data to the virtual machine module 118.Virtual machine module 118 (FIG. 1A) may spawn at least two virtualmachines 11, 12, and/or 13, and transfer the data to at least one ofvirtual machines 11, 12, and/or 13. FIG. 1B shows virtual machines 11,12, and 13 encompassing a virtual machine representation of real machine130, post (e.g., subsequent to) activation of Link 1, Link 2, and Link3, respectively (e.g., as at least a part of real machine 130 wouldexist had Link 1, Link 2, and/or Link 3 actually been traversed on realmachine 130). FIG. 1B further depicts virtual machines 11, 12, and/or 13including a virtual machine representation of content of the realmachine 130 post activation of Link 1, Link 2, and/or Link 3,respectively. Examples of such content include a movie, music file, ascript (e.g., Java script or Active X control), a markup language, anemail, etc. downloaded onto real machine 130 from one or more sourcesassociated with activation/traversal of Link 1, Link 2, and/or Link 3.An example of determining an acceptability of an effect of the data atleast in part via at least two virtual machine representations of atleast a part of an end user's real machine may include determining anacceptability of an effect of the data at least in part via a virtualmachine representation of at least a portion of the content of the realmachine and a virtual machine representation of at least a portion ofhardware of the real machine, for example, the state of virtual machine11 and the state of virtual machine 12 after loading at least a portionof the data contained in Link 1.

The operation 704 illustrates determining an acceptability of an effectof the retrieved at least a portion of the data on at least two virtualmachine representations of at least a part of an end user's real machinehaving one or more end-user specified preferences, at least one of theat least two virtual machine representations operating on a separatecore of a system comprising at least two cores. Continuing the exampleabove, FIG. 1A shows the Effect of data acceptability determinationengine 106. Effect of data acceptability determination engine 106 mayreceive data from data retriever engine 102. FIG. 1A further illustratesthe Effect of data acceptability determination engine 106 furtherincluding a virtual machine module 118 and a user preference database120. Effect of data acceptability determination engine 106 may transferthe data to the virtual machine module 118. Virtual machine module 118(FIG. 1A) may spawn at least one virtual machine 11, 12, and/or 13, andtransfer the data to at least one of virtual machines 11, 12, and/or 13.As illustrated in FIGS. 1B and 1C, each of virtual machine 11, virtualmachine 12, virtual machine 13, virtual machine 21, virtual machine 22,and virtual machine 23 may operate on an individual core 11, 12, 13, 31,32, 33, respectively, of a multi-core processor, or virtual machine 11may run on one core and virtual machines 12, 13 may run on the othercore of a dual core processor such as an Intel® dual core processor andso on. The multi-core processor may include a plurality of processorcores packaged in one processor package. The term core as used hereinmay refer, for example, to a single processor of a multiprocessorsystem, or to a processor core of a multi-core processor. Multi-coreprocessor may be utilized as portable computers such as laptopcomputers, personal digital assistants, or desktop computers, orservers, or another form of processor based system. Combinations ofthese types of platforms may be present. The multi-core system mayinclude a multi-core processor, each core comprising a separate addressspace, and having internal to that address space.

The operation 706 illustrates determining an acceptability of an effectof the retrieved at least a portion of the data on at least two virtualmachine representations of at least a part of an end user's real machinehaving one or more end-user specified preferences. Continuing theexample above, FIG. 1A shows the Effect of data acceptabilitydetermination engine 106. Effect of data acceptability determinationengine 106 may receive data from data retriever engine 102. FIG. 1Afurther illustrates the Effect of data acceptability determinationengine 106 further including a virtual machine module 118 and a userpreference database 120. Effect of data acceptability determinationengine 106 may transfer the data to the virtual machine module 118.Virtual machine module 118 (FIG. 1A) may spawn at least one virtualmachine 11, 12, and/or 13, and transfer the data to at least two ofvirtual machines 11, 12, and/or 13. At least one of virtual machines 11,12, and/or 13 may operate on a separate operating system at a locationof the data (e.g. executed on a subsystem, such as the virtual machinemodule 118 (FIG. A) including a plurality of virtual machines 11, 12,and/or 13 (FIG. 1B) within the real machine 130).

The operation 708 illustrates determining an acceptability of an effectof the retrieved at least a portion of the data on at least two virtualmachine representations of at least a part of an end user's real machinehaving one or more end-user specified preferences, at least one of theat least two virtual machine representations operating on a separateoperating system at a location of the retrieved at least a portion ofthe data. Continuing the example above, FIG. 1A shows the Effect of dataacceptability determination engine 106. Effect of data acceptabilitydetermination engine 106 may receive data from data retriever engine102. FIG. 1A further illustrates the Effect of data acceptabilitydetermination engine 106 further including a virtual machine module 118and a user preference database 120. Effect of data acceptabilitydetermination engine 106 may transfer the data to the virtual machinemodule 118. Virtual machine module 118 (FIG. 1A) may spawn at least onevirtual machine 11, 12, and/or 13, and transfer the data to at least oneof virtual machines 11, 12, and/or 13. FIG. 1B further illustratesvirtual machines 11, 12, 13. In one implementation, any of virtualmachines 11, 12, 13 may be generated on the real machine 130 (e.g. as asubsystem of real machine 130).

The operation 710 illustrates determining an acceptability of an effectof the retrieved at least a portion of the data on at least two virtualmachine representations of at least a part of an end user's real machinehaving one or more end-user specified preferences, at least one of theat least two virtual machine representations operating on a separateoperating system at a location geographically distinct from a locationof the retrieved at least a portion of the data. Continuing the exampleabove, FIG. 1A shows the Effect of data acceptability determinationengine 106. Effect of data acceptability determination engine 106 mayreceive data from data retriever engine 102. FIG. 1A further illustratesthe Effect of data acceptability determination engine 106 furtherincluding a virtual machine module 118 and a user preference database120. Effect of data acceptability determination engine 106 may transferthe data to the virtual machine module 118. Virtual machine module 118(FIG. 1A) may spawn at least one virtual machine 11, 12, and/or 13, andtransfer the data to at least one of virtual machines 11, 12, and/or 13.At least one virtual machine, for example virtual machine 12, may bevirtual machines operating at geographically distinct location such as aremote server, or a remote system configured to receive and examine realmachine information transferred to the remote system and duplicate datafrom the real machine 130. In some instances, each virtual machine maybe generated on one or more separate cores of a multi-core processor. Inanother embodiment, determining an acceptability of an effect of theretrieved at least a portion of the data on at least two virtual machinerepresentations of at least a part of an end user's real machine havingone or more end-user specified preferences, at least one of the at leasttwo virtual machine representations operating on a separate operatingsystem at a location geographically distinct from a location of theretrieved at least a portion of the data may include gatheringparameters of an end user's real machine to assist in generating avirtual duplicate of the end user's real machine at a remote locationsuch as a remote server or remote computing system (e.g. hosted on,running on, or being implemented on the remote server or remote system).

FIG. 8 illustrates alternative embodiments of the example operationalflow 200 of FIG. 2. FIG. 8 illustrates example embodiments where theoperation 220 may include at least one additional operation. Additionaloperations may include an operation 802, an operation 804, and/or anoperation 806.

The operation 802 illustrates determining a state change of a virtualmachine representation between a prior state and a subsequent state ofthe virtual machine representation after loading at least a portion ofdata. Continuing the example above, FIG. 1A shows the Effect of dataacceptability determination engine 106. Effect of data acceptabilitydetermination engine 106 may receive data from data retriever engine102. FIG. 1A further illustrates the Effect of data acceptabilitydetermination engine 106 further including a virtual machine module 118and a user preference database 120. Effect of data acceptabilitydetermination engine 106 may transfer the data to the virtual machinemodule 118. Virtual machine module 118 (FIG. 1A) may spawn at least onevirtual machine 11, 12, and/or 13, and transfer the data to at least oneof virtual machines 11, 12, and/or 13. A state change (e.g., a decreasein memory) of at least one of virtual machines 11, 12, and/or 13 (FIG.1B) may be determined by a component of at least one of virtual machines11, 12, and/or 13 measuring a characteristic of the virtual machinerepresentation of the content, software, hardware or operating system ofthe real machine 130 before and after the at least a portion of data hasloaded. For instance, a state change may be measured after a searchresult containing a plurality of web links has loaded and at least oneweb link has been activated.

The operation 804 illustrates determining a state of a virtual machinerepresentation prior to loading at least a portion of data. Continuingthe example above, FIG. 1A shows the Effect of data acceptabilitydetermination engine 106. Effect of data acceptability determinationengine 106 may receive data from data retriever engine 102. FIG. 1Afurther illustrates the Effect of data acceptability determinationengine 106 further including a virtual machine module 118 and a userpreference database 120. Effect of data acceptability determinationengine 106 may transfer the data to the virtual machine module 118.Virtual machine module 118 (FIG. 1A) may spawn at least one virtualmachine 11, 12, and/or 13, and transfer the data to at least one ofvirtual machines 11, 12, and/or 13. At least one of virtual machines 11,12, and/or 13 (FIG. 1B) may determine a state of at least one component(e.g., the hardware) of the virtual machine prior to activation (e.g.,before) of a link. Virtual machine state may be representative of astate for all or at least a portion of the components (e.g., content,software, hardware, operating system) of the real machine 130represented by the virtual machine 11, 12, and/or 13. For instance, atleast one of virtual machines 11, 12, and/or 13 may be determined to befree of viruses, an amount of virtual machine memory may be measured, ora processing speed of at least one of virtual machines 11, 12, and/or 13may be determined. At least one of virtual machines 11, 12, and/or 13may contain a diagnostic application configured to analyze virtualmachine performance and contents.

The operation 806 illustrates determining a state of a virtual machinerepresentation after loading at least a portion of data. Continuing theexample above, FIG. 1A shows the Effect of data acceptabilitydetermination engine 106. Effect of data acceptability determinationengine 106 may receive data from data retriever engine 102. FIG. 1Afurther illustrates the Effect of data acceptability determinationengine 106 further including a virtual machine module 118 and a userpreference database 120. Effect of data acceptability determinationengine 106 may transfer the data to the virtual machine module 118.Virtual machine module 118 (FIG. 1A) may spawn at least one virtualmachine 11, 12, and/or 13, and transfer the data to at least one ofvirtual machines 11, 12, and/or 13. At least one of virtual machines 11,12, and/or 13 (FIG. 1B) may determine a state of at least one component(e.g., the hardware) of the virtual machine subsequent to (e.g., after)activation of a link. For instance a virtual machine state may berepresentative of a state for all characteristics of the real machine130 content, software, hardware or operating system represented by atleast one of virtual machines 11, 12, and/or 13 after at least a portionof the data has loaded. For instance, at least one of virtual machines11, 12, and/or 13 may be determined to contain a virus, an amount ofvirtual machine memory may be measured, or a processing speed of atleast one of virtual machines 11, 12, and/or 13 may be determined. Atleast one of virtual machines 11, 12, and/or 13 may be examined todetermine, for example, if a virus or any other undesired software ispresent on the machine after at least a portion of the data has loadedby examining the virtual machine representation of the operating systemof the real machine 130 (FIG. 1B), or if information from the realmachine 130 has been transferred to an external location by examiningthe software of the real machine 130.

FIG. 9 illustrates alternative embodiments of the example operationalflow 200 of FIG. 2. FIG. 9 illustrates example embodiments where theoperation 220 may include at least one additional operation. Additionaloperations may include an operation 902.

The operation 902 illustrates determining whether the state change is anundesirable state change based on one or more end-user specifiedpreferences. Continuing the example above, FIG. 1A shows the Effect ofdata acceptability determination engine 106. Effect of dataacceptability determination engine 106 may receive data from dataretriever engine 102. FIG. 1A further illustrates the Effect of dataacceptability determination engine 106 further including a virtualmachine module 118 and a user preference database 120. Effect of dataacceptability determination engine 106 may transfer the data to thevirtual machine module 118. Virtual machine module 118 (FIG. 1A) mayspawn at least one virtual machine 11, 12, and/or 13, and transfer thedata to at least one of virtual machines 11, 12, and/or 13. Anundesirable state change may be determined by examining the changes toat least one of virtual machines 11, 12, and/or 13 (FIG. 1B) andcomparing the state change of at least one of virtual machines 11, 12,and/or 13 to user preference database information spawned on at leastone of virtual machines 11, 12, and/or 13 by a transfer of userpreference database information from the user preference database 120(FIG. 1A) to the virtual machine module 118 (FIG. 1A) which spawns acopy of at least a portion of the user preference database 120 (FIG. 1A)onto at least one of virtual machines 11, 12, and/or 13. An undesirablestate change may include any undesirable state change including, but notlimited to, a decrease in memory or processing speed and/or the presenceof a virus or other undesirable software after at least a portion of thedata has loaded. Undesirable state changes may further include anundesirable transfer of information located on at least one of virtualmachines 11, 12, and/or 13 to an external location, an undesirabletransfer of data onto at least one of virtual machines 11, 12, and/or 13from an external location after at least a portion of the data hasloaded on at least one of virtual machines 11, 12, and/or 13 that mayresult in an undesired change in the state of content, software,hardware or an operating system of the real machine 130 and/or anundesirable transfer of data onto at least one of virtual machines 11,12, and/or 13 where at least a portion of the transferred data may befound objectionable when viewed by a user 10.

FIG. 10 illustrates alternative embodiments of the example operationalflow 200 of FIG. 2. FIG. 10 illustrates example embodiments where theoperation 220 may include at least one additional operation. Additionaloperations may include an operation 1002, an operation 1004, and/or anoperation 1006.

The operation 1002 illustrates determining an acceptability of an effectof the retrieved at least a portion of the data at least in part via avirtual machine representation of at least a part of an end user's realmachine having one or more end-user specified preferences within anacceptable amount of user interface time. Continuing the example above,FIG. 1A illustrates an Effect of data acceptability determination engine106. Effect of data acceptability determination engine 106 may receivedata from data retriever engine 102. Effect of data acceptabilitydetermination engine 106 (FIG. 1A) may utilize, for example, virtualmachine 12 (FIG. 1A) spawned by virtual machine module 118 to determinewhether data associated with Link 2 would result in a change in theoperating system of real machine 130 contra to a user preferenceregarding the operating system as stored in the user preference database120. Effect of data acceptability determination may be determined withinan acceptable amount of user interface time (e.g. a tolerable wait timefor information retrieval). An acceptable amount of user interface timemay be within a range from an amount of time approximating aninstantaneous effect of data acceptability determination to an amount oftime approximating a maximum time a user may be willing to wait for aresult before abandoning a data retrieval (e.g., a downloading webpage).

The operation 1004 illustrates determining an acceptability of an effectof the retrieved at least a portion of the data at least in part via avirtual machine representation of at least a part of an end user's realmachine having one or more end-user specified preferences inapproximately one-tenth of a second. Continuing the example above,Effect of data acceptability determination engine 106 (FIG. 1A) mayutilize, for example, virtual machine 12 (FIG. 1A) spawned by virtualmachine module 118 to determine whether data associated with Link 2would result in a change in the operating system of real machine 130contra to a user preference regarding the operating system as stored inthe user preference database 120. A user interface time of approximatelyone-tenth of a second may approximate an acceptable amount elapsed timefor a user to feel that the Effect of data acceptability determinationengine 106 is reacting instantaneously.

The operation 1006 illustrates determining an acceptability of an effectof the retrieved at least a portion of the data at least in part via avirtual machine representation of at least a part of an end user's realmachine having one or more end-user specified preferences in less thanapproximately 1 second. Continuing the example above, Effect of dataacceptability determination engine 106 (FIG. 1A) may utilize, forexample, virtual machine 12 (FIG. 1A) spawned by virtual machine module118 to determine whether data associated with Link 2 would result in achange in the operating system of real machine 130 contra to a userpreference regarding the operating system as stored in the userpreference database 120. A user interface time of less thanapproximately one second may approximate an acceptable amount elapsedtime for a user to notice a delay in the Effect of data acceptabilitydetermination without abandoning the information retrieval.

FIG. 11 illustrates alternative embodiments of the example operationalflow 200 of FIG. 2. FIG. 11 illustrates example embodiments where theoperation 220 may include at least one additional operation. Additionaloperations may include an operation 1102, an operation 1104, and/or anoperation 1106.

The operation 1102 illustrates determining an acceptability of an effectof the retrieved at least a portion of the data in response to at leastone user setting. Continuing the example above, FIG. 1A shows the Effectof data acceptability determination engine 106. Effect of dataacceptability determination engine 106 may receive data from dataretriever engine 102. FIG. 1A further illustrates the Effect of dataacceptability determination engine 106 further including a virtualmachine module 118 and a user preference database 120. Effect of dataacceptability determination engine 106 may transfer the data to thevirtual machine module 118. Virtual machine module 118 (FIG. 1A) mayspawn at least one virtual machine 11, 12, and/or 13, and transfer thedata to at least one of virtual machines 11, 12, and/or 13. Anacceptability of an effect of the data may be determined by determiningif a state change to at least one of virtual machines 11, 12, and/or 13has occurred and comparing the state change of at least one of virtualmachines 11, 12, and/or 13 to user preference database informationspawned on at least one of virtual machines 11, 12, and/or 13.Comparison may be made, for example, by transferring user preferencedatabase information from the user preference database 120 (FIG. 1A) tothe virtual machine module 118 (FIG. 1A) which spawns a copy of at leasta portion of the user preference database 120 (FIG. 1A) onto at leastone of virtual machines 11, 12, and/or 13. User preference database 120may include at least one end-user specified preference relating to atleast one of content, software, hardware and/or an operating system of areal machine 130. At least one of virtual machines 11, 12, and/or 13 maydetermine an acceptability of an effect of the data based on at leastone user setting contained in a user preference database at least aportion of which may be spawned onto at least one of virtual machines11, 12, and/or 13 via virtual machine module 118 (e.g., does a websitecontain only images, text, audio or visual data suitable for viewing bya user based on a setting established by a user such as a political orcultural preference setting). Further examples of user preferencesinclude specific religion or lifestyle preference, such as “return onlylinks relating to Roman Catholicism” or “return only links relating to avegan lifestyle” that may be stored in the real machine 130.User-specific preference may also relate to user information safety orcomputer safety, such as “do not display links requesting informationfrom my computer,” or “do not display links that transfer viruses ontomy computer.”

The operation 1104 illustrates determining an acceptability of an effectof the retrieved at least a portion of the data in response to apersonal user setting. Continuing the example above, FIG. 1A shows theEffect of data acceptability determination engine 106. Effect of dataacceptability determination engine 106 may receive data from dataretriever engine 102. FIG. 1A further illustrates the Effect of dataacceptability determination engine 106 further including a virtualmachine module 118 and a user preference database 120. Effect of dataacceptability determination engine 106 may transfer the data to thevirtual machine module 118. Virtual machine module 118 (FIG. 1A) mayspawn at least one virtual machine 11, 12, and/or 13, and transfer thedata to at least one of virtual machines 11, 12, and/or 13. Userpreference database information stored in the user preference database120 (FIG. 1A) may be transferred to the virtual machine module 118 (FIG.1A), which spawns a copy of at least a portion of the user preferencedatabase 120 (FIG. 1A) onto at least one of virtual machines 11, 12,and/or 13. At least one of virtual machines 11, 12, and/or 13 (FIG. 1B)may compare the data received from the virtual machine module 118 (FIG.1A) to a personal user setting (e.g., “show only automobile relateddata”) contained in user preference database information spawned on atleast one of virtual machines 11, 12, and/or 13. User preferencedatabase 120 may include at least one personal user setting relating toat least one of content, software, hardware and/or an operating systemof a real machine 130. Personal user setting may be a setting input by auser that is personal to the user, such as an information securitylevel, a content filter level, or a personal desirability setting suchas “show only non-religious data” or “show only automobile relateddata.”

The operation 1106 illustrates determining an acceptability of an effectof the retrieved at least a portion of the data in response to a peeruser setting. Continuing the example above, user preference database 120may include at least one peer user setting relating to at least one ofcontent, software, hardware and/or an operating system of an end user'sreal machine 130. Peer user setting may be a setting input by a userthat is determined by a peer group, such as a peer group determinedinformation security level such as “display only 100 percent securewebsites”, a peer group determined data filter level such as “filter100% of obscene data”, or a peer group desirability setting such as“show only classical music related data” or “show only knitting relateddata.”

FIG. 12 illustrates alternative embodiments of the example operationalflow 200 of FIG. 2. FIG. 12 illustrates example embodiments where theoperation 220 may include at least one additional operation. Additionaloperations may include an operation 1202, and/or an operation 1204. Theoperation 1202 illustrates determining an acceptability of an effect ofthe retrieved at least a portion of the data in response to a corporateuser setting. Continuing the example above, at least one of virtualmachines 11, 12, and/or 13 (FIG. 1B) may compare the data received fromthe virtual machine module 118 (FIG. 1A) to a corporate user settingcontained in user preference database information spawned on at leastone of virtual machines 11, 12, and/or 13. User preference database 120may include at least one corporate user setting relating to at least oneof content, software, hardware and/or an operating system of an enduser's real machine 130. Corporate user setting may be a setting inputby a corporation that is determined to the corporation, such as acorporate desirability setting such as “show only real-estate relateddata” or “show only agricultural related data.”

The operation 1204 illustrates determining an acceptability of an effectof the retrieved at least a portion of the data in response to a worksafety user setting. Continuing the example above, at least one ofvirtual machines 11, 12, and/or 13 (FIG. 1B) may compare the datareceived from the virtual machine module 118 (FIG. 1A) to a work safetyuser setting contained in user preference database information spawnedon at least one of virtual machines 11, 12, and/or 13. User preferencedatabase 120 may include at least one work safety user setting relatingto at least one of content, software, hardware and/or an operatingsystem of an end user's real machine 130. Thus, in one specific example,a webpage or website data may be determined to be displayable if thedata satisfies a work safety user setting such as a corporateinformation security level, corporate user setting, or a corporateinformation content filter level corporate user setting.

FIG. 13 illustrates alternative embodiments of the example operationalflow 200 of FIG. 2. FIG. 13 illustrates example embodiments where theoperation 220 may include at least one additional operation. Additionaloperations may include an operation 1302, an operation 1304, and/or anoperation 1306. The operation 1302 illustrates determining anacceptability of an effect of the retrieved at least a portion of thedata in response to a desirability setting. Continuing the exampleabove, at least one of virtual machines 11, 12, and/or 13 (FIG. 1B) maycompare the data received from the virtual machine module 118 (FIG. 1A)to a desirability setting (e.g., does a website contain only images,text, audio or visual data suitable for viewing by a user based on adesirability setting established by a user such as a desire to view onlynon-obscene material) contained in user preference database informationspawned on at least one of virtual machines 11, 12, and/or 13. Userpreference database 120 may include at least one desirability settingrelating to at least one of content, software, hardware and/or anoperating system of an end user's real machine 130.

The operation 1304 illustrates determining an acceptability of an effectof the retrieved at least a portion of the data in response to areligious desirability setting. Continuing the example above, at leastone of virtual machines 11, 12, and/or 13 may compare the data receivedfrom the virtual machine module 118 to a religious desirability setting(e.g., does a website contain only images, text, audio or visual datasuitable for viewing by a user based on a religious desirability settingestablished by a user such as a desire to view only Hindu material)contained in user preference database information spawned on at leastone of virtual machines 11, 12, and/or 13. A religious desirabilitysetting may be include any setting regarding a major, minor, or otherreligion such as Christianity, Judaism, Islam, Hinduism, and so on.

The operation 1306 illustrates determining an acceptability of an effectof the retrieved at least a portion of the data in response to apolitical desirability setting. Continuing the example above, at leastone of virtual machines 11, 12, and/or 13 (FIG. 1B) may compare the datareceived from the virtual machine module 118 (FIG. 1A) to a politicaldesirability setting (e.g., does a website contain only images, text,audio or visual data suitable for viewing by a user based on a politicaldesirability setting established by a user such as a desire to view onlyDemocratic Party material) contained in user preference databaseinformation spawned on at least one of virtual machines 11, 12, and/or13. A political desirability setting may include any setting regarding apolitical party or affiliation (e.g., Republican, Democratic,Libertarian, Green Party, etc.).

FIG. 14 illustrates alternative embodiments of the example operationalflow 200 of FIG. 2. FIG. 14 illustrates example embodiments where theoperation 220 may include at least one additional operation. Additionaloperations may include an operation 1402, and/or an operation 1404. Theoperation 1402 illustrates determining an acceptability of an effect ofthe retrieved at least a portion of the data in response to a culturaldesirability setting. Continuing the example above, at least one ofvirtual machines 11, 12, and/or 13 (FIG. 1B) may compare the datareceived from the virtual machine module 118 (FIG. 1A) to a culturaldesirability setting (e.g., does a website contain only images, text,audio or visual data suitable for viewing by a user based on a culturaldesirability setting established by a user such as a desire to view onlymaterials regarding early Mayan civilization) contained in userpreference database information spawned on at least one of virtualmachines 11, 12, and/or 13. A cultural desirability setting may includeany culturally related information such as a religious, ethnic,regional, or heritage based cultural desirability setting or any othercultural desirability setting.

The operation 1404 illustrates determining an acceptability of an effectof the retrieved at least a portion of the data in response to a themerelated desirability setting. Continuing the example above, at least oneof virtual machines 11, 12, and/or 13 (FIG. 1B) may compare the datareceived from the virtual machine module 118 (FIG. 1A) to a themerelated desirability setting (e.g., does a website contain only images,text, audio or visual data suitable for viewing by a user based on atheme related desirability setting established by a user such as adesire to view only materials regarding collectible stamps) contained inuser preference database information spawned on at least one of virtualmachines 11, 12, and/or 13. A theme related desirability setting mayinclude any theme related information, such as information relating tocars, fashion, electronics, sports, hobbies, collector's items, or anytheme or category that may be of interest to a user.

FIG. 15 illustrates alternative embodiments of the example operationalflow 200 of FIG. 2. FIG. 15 illustrates example embodiments where theoperation 220 may include at least one additional operation. Additionaloperations may include an operation 1502. The operation 1502 illustratesdetermining an acceptability of an effect of the retrieved at least aportion of the data in response to an age appropriateness desirabilitysetting. Continuing the example above, at least one of virtual machines11, 12, and/or 13 (FIG. 1B) may compare the data received from thevirtual machine module 118 (FIG. 1A) to an age appropriatenessdesirability setting (e.g., does a website contain only images, text,audio or visual data suitable for viewing by a user based on an ageappropriateness desirability setting established by a user such as adesire to view only materials given a PG or lower rating as determinedby the Motion Picture of America Association film rating system)contained in user preference database information spawned on at leastone of virtual machines 11, 12, and/or 13. An age appropriatenessdesirability setting may include any age appropriate setting, such as arating threshold or a profanity threshold.

FIG. 16 illustrates alternative embodiments of the example operationalflow 200 of FIG. 2. FIG. 16 illustrates example embodiments where theoperation 220 may include at least one additional operation. Additionaloperations may include an operation 1602, an operation 1604, and/or anoperation 1606.

The operation 1602 illustrates determining an acceptability of an effectof the retrieved at least a portion of the data in response to at leastone privacy related setting. Continuing the example above, at least oneof virtual machines 11, 12, and/or 13 (FIG. 1B) may compare the datareceived from the virtual machine module 118 (FIG. 1A) to a privacyrelated setting (e.g., does a website contain only images, text, audioor visual data suitable for viewing by a user based on a privacy relatedsetting established by a user) contained in user preference databaseinformation spawned on at least one of virtual machines 11, 12, and/or13. A privacy related setting may include any privacy related settings(e.g., does a website contain only data that will not requestinformation from my computer or allow others to view personalinformation saved on my computer).

The operation 1604 illustrates determining an acceptability of an effectof the retrieved at least a portion of the data in response to a userspecific privacy related setting. Continuing the example above, at leastone of virtual machines 11, 12, and/or 13 (FIG. 1B) may compare the datareceived from the virtual machine module 118 (FIG. 1A) to a userspecific privacy related setting (e.g., will a website request specificinformation about the user such as name, address, telephone number)contained in user preference database information spawned on at leastone of virtual machines 11, 12, and/or 13. A user specific privacyrelated setting may include any user specific privacy related settings(e.g., a setting relating to a user's biographical information orfinancial information).

The operation 1606 illustrates determining an acceptability of an effectof the retrieved at least a portion of the data in response to a groupprivacy related setting. Continuing the example above, at least one ofvirtual machines 11, 12, and/or 13 (FIG. 1B) may compare the datareceived from the virtual machine module 118 (FIG. 1A) to a groupprivacy related setting (e.g., will a website request information aboutan organization such as name, address, telephone number) contained inuser preference database information spawned on at least one of virtualmachines 11, 12, and/or 13. A group privacy related setting may includeany group privacy related settings (e.g., a setting relating to agroup's membership). Group privacy related setting may be any settingestablished by a group such as a work group (e.g., employees of acompany), a peer group (e.g., members of a book club), or a family group(e.g., members of family unit) privacy related setting.

FIG. 17 illustrates alternative embodiments of the example operationalflow 200 of FIG. 2. FIG. 17 illustrates example embodiments where theoperation 220 may include at least one additional operation. Additionaloperations may include an operation 1702, and/or an operation 1704. Theoperation 1702 illustrates determining an acceptability of an effect ofthe retrieved at least a portion of the data in response to a corporateprivacy related setting. Continuing the example above, at least one ofvirtual machines 11, 12, and/or 13 (FIG. 1B) may compare the datareceived from the virtual machine module 118 (FIG. 1A) to a corporateprivacy related setting (e.g., will a website request information abouta corporation such as data stored on a real machine belonging to thecorporation) contained in user preference database information spawnedon at least one of virtual machines 11, 12, and/or 13. Corporate privacyrelated setting may be determined by a corporate issued privacy manual,or other such document or mandate set forth by officers of acorporation.

The operation 1704 illustrates determining an acceptability of an effectof the retrieved at least a portion of the data in response to a type oftransmitted user information. Continuing the example above, at least oneof virtual machines 11, 12, and/or 13 (FIG. 1B) may compare the datareceived from the virtual machine module 118 (FIG. 1A) to at least oneacceptable type of transmitted user information setting (e.g., do notreturn links that will transmit my e-mail address, home address ortelephone number to an external location) contained in user preferencedatabase information spawned on at least one of virtual machines 11, 12,and/or 13. Acceptable type of transmitted user information setting maybe determined by a user 10 (FIG. 1B). For instance, acceptability of theeffect of the data may be determined in response to whether or notprivate user information, such as credit card numbers, bank accounts,personal identification information or any other personal userinformation may be transmitted to a location external to the realmachine by selecting the link.

FIG. 18 illustrates alternative embodiments of the example operationalflow 200 of FIG. 2. FIG. 18 illustrates example embodiments where theoperation 220 may include at least one additional operation. Additionaloperations may include an operation 1802, and/or an operation 1804. Theoperation 1802 illustrates determining an acceptability of an effect ofthe retrieved at least a portion of the data in response to a type ofcaptured user information. Continuing the example above, at least one ofvirtual machines 11, 12, and/or 13 (FIG. 1B) may compare the datareceived from the virtual machine module 118 (FIG. 1A) to at least oneacceptable type of captured user information setting (e.g., do notreturn links that will capture my e-mail address, home address ortelephone number) contained in user preference database informationspawned on at least one of virtual machines 11, 12, and/or 13.Acceptable type of captured user information setting may be determinedby a user 10 (FIG. 1B). For instance, acceptability of the effect of thedata may be determined in response to whether or not private userinformation, such as credit card numbers, bank accounts, personalidentification information or any other personal user information may becaptured by a machine located at a location external to the real machineby selecting the link.

The operation 1804 illustrates determining an acceptability of an effectof the retrieved at least a portion of the data in response to a type ofexposed user information. Continuing the example above, at least one ofvirtual machines 11, 12, and/or 13 (FIG. 1B) may compare the datareceived from the virtual machine module 118 (FIG. 1A) to at least oneacceptable type of exposed user information setting (e.g., do not returnlinks that will expose personal financial information stored on the realmachine 130) contained in user preference database information spawnedon at least one of virtual machines 11, 12, and/or 13. Acceptable typesof exposed user information settings may be determined by a user 10(FIG. 1B). For instance, acceptability of the effect of the data may bedetermined in response to whether or not private user information, suchas credit card numbers, bank accounts, personal identificationinformation or any other personal user information may be exposed to amachine located at a location external to the real machine by selectingthe link.

FIG. 19 illustrates alternative embodiments of the example operationalflow 200 of FIG. 2. FIG. 19 illustrates example embodiments where theoperation 220 may include at least one additional operation. Additionaloperations may include an operation 1902, an operation 1904, and/or anoperation 1906.

The operation 1902 illustrates determining an acceptability of an effectof the retrieved at least a portion of the data in response to visuallyexamining a data image. Continuing the example above, FIG. 1A shows theEffect of data acceptability determination engine 106. Effect of dataacceptability determination engine 106 may receive data from dataretriever engine 102. FIG. 1A further illustrates the Effect of dataacceptability determination engine 106 further including a virtualmachine module 118 and a user preference database 120. Effect of dataacceptability determination engine 106 may transfer the data to thevirtual machine module 118. Virtual machine module 118 (FIG. 1A) mayspawn at least one virtual machine 11, 12, and/or 13, and transfer thedata to at least one of virtual machines 11, 12, and/or 13. To visuallyexamine a data image, at least one of virtual machines 11, 12, and/or 13(FIG. 1B) may include an image scanning module. In one embodiment,visually examining a data image may include computer implemented imageanalysis, such as, color analysis, pattern-matching,pattern-recognition, or any other technique for recognizing a particularimage or type of image.

The operation 1904 illustrates determining an acceptability of an effectof the retrieved at least a portion of the data at least in part via avirtual machine representation of at least a portion of content of anend user's real machine having one or more end-user specifiedpreferences. Continuing the example above, FIG. 1A shows the Effect ofdata acceptability determination engine 106. Effect of dataacceptability determination engine 106 may receive data from dataretriever engine 102. FIG. 1A further illustrates the Effect of dataacceptability determination engine 106 further including a virtualmachine module 118 and a user preference database 120. Effect of dataacceptability determination engine 106 may transfer the data to thevirtual machine module 118. Virtual machine module 118 (FIG. 1A) mayspawn at least one virtual machine 11, 12, and/or 13, and transfer thedata to at least one of virtual machines 11, 12, and/or 13. FIG. 1Bshows virtual machines 11, 12, and 13 encompassing a virtual machinerepresentation of real machine 130, post (e.g., subsequent to)activation of Link 1, Link 2, and Link 3, respectively (e.g., as atleast a part of real machine 130 would exist had Link 1, Link 2, and/orLink 3 actually been traversed on real machine 130). FIG. 1B furtherdepicts virtual machines 11, 12, and 13 including a virtual machinerepresentation of content of the real machine 130 post activation ofLink 1, Link 2, and/or Link 3, respectively. Examples of such contentinclude a movie, music file, a script (e.g., Java script or Active Xcontrol), a markup language, an email, etc. downloaded onto real machine130 from one or more sources associated with activation/traversal ofLink i, Link 2, and/or Link 3. An example of determining anacceptability of an effect of the data at least in part via a virtualmachine representation may include determining an acceptability of aneffect of the data at least in part via a virtual machine representationof at least a portion of the content of the real machine includedetermining whether or not a video or image has been loaded onto, forexample, the virtual machine 11 after loading at least a portion of thedata contained in Link 1.

The operation 1906 illustrates determining an acceptability of an effectof the retrieved at least a portion of the data at least in part via avirtual machine representation of at least a portion of software of anend user's real machine having one or more end-user specifiedpreferences. Continuing the example above, FIG. 1A shows the Effect ofdata acceptability determination engine 106. Effect of dataacceptability determination engine 106 may receive data from dataretriever engine 102. FIG. 1A further illustrates the Effect of dataacceptability determination engine 106 further including a virtualmachine module 118 and a user preference database 120. Effect of dataacceptability determination engine 106 may transfer the data to thevirtual machine module 118. Virtual machine module 118 (FIG. 1A) mayspawn at least one virtual machine 11, 12, and/or 13, and transfer thedata to at least one of virtual machines 11, 12, and/or 13. FIG. 1Bshows virtual machines 11, 12, and 13 encompassing a virtual machinerepresentation of real machine 130, post (e.g., subsequent to)activation of Link 1, Link 2, and Link 3, respectively (e.g., as atleast a part of real machine 130 would exist had Link 1, Link 2, and/orLink 3 actually been traversed on real machine 130). FIG. 1B illustratesvirtual machine 11 including a virtual machine representation ofsoftware (e.g. a state of software, such as a state of Windows MediaPlayer) of the real machine 130 post (e.g., subsequent to) activation ofLink 1. Examples of such software might include a commercial wordprocessing program or suite of programs (e.g., Microsoft® Office forWindows), an open source Web browser (e.g., Mozilla's Firefox® Browser),an AJAX mash up (e.g., an executing JavaScript™ and/or data obtained bysame via an XML-like scheme), a commercial database management system(e.g., one or more of Oracle Corporation's various products), acommercial anti-malware/spyware program (e.g., such as those of SymantecCorporation or McAfee, Inc.), a multi-media program (e.g., QuickTime)etc. An example of determining an acceptability of an effect of the dataat least in part via a virtual machine representation may includedetermining an acceptability of an effect of the content of the data atleast in part via a virtual machine representation of at least a portionof the software of the real machine include determining whether or notan unauthorized program or suite of programs (e.g., music downloadingsoftware) has been loaded, for example, onto virtual machine 12 afterloading at least a portion of the data contained in Link 2.

FIG. 20 illustrates alternative embodiments of the example operationalflow 200 of FIG. 2. FIG. 20 illustrates example embodiments where theoperation 220 may include at least one additional operation. Additionaloperations may include an operation 1902, an operation 2004, and/or anoperation 2006.

The operation 2002 illustrates determining an acceptability of an effectof the retrieved at least a portion of the data at least in part via avirtual machine representation of at least a portion of hardware of anend user's real machine having one or more end-user specifiedpreferences. Continuing the example above, FIG. 1A shows the Effect ofdata acceptability determination engine 106. Effect of dataacceptability determination engine 106 may receive data from dataretriever engine 102. FIG. 1A further illustrates the Effect of dataacceptability determination engine 106 further including a virtualmachine module 118 and a user preference database 120. Effect of dataacceptability determination engine 106 may transfer the data to thevirtual machine module 118. Virtual machine module 118 (FIG. 1A) mayspawn at least one virtual machine 11, 12, and/or 13, and transfer thedata to at least one of virtual machines 11, 12, and/or 13. FIG. 1Billustrates virtual machine 11 including a virtual machinerepresentation of hardware (e.g., a state of the hardware) of the realmachine 130 post activation of Link 1. Examples of such hardware mightinclude all or part of a chipset (e.g., data processor and/or graphicsprocessor chipsets such as those of Intel Corporation and/orNvidiaCorporation), a memory chip (e.g., flash memory and/or randomaccess memories such as those of Sandisk Corporation and/or SamsungElectronics, Co., LTD), a data bus, a hard disk (e.g., such as those ofSeagate Technology, LLC), a network adapter (e.g., wireless and/or wiredLAN adapters such as those of Linksys and/or CiscoTechnology, Inc.),printer, a removable drive (e.g., flash drive), a cell phone, etc. Anexample of determining an acceptability of an effect of the data atleast in part via a virtual machine representation includes determiningan acceptability of an effect of the data at least in part via a virtualmachine representation of at least a portion of the hardware of the realmachine includes determining whether a network adapter on, for example,virtual machine 12 has been disabled after loading at least a portion ofthe data contained in Link 2.

The operation 2004 illustrates determining an acceptability of an effectof the retrieved at least a portion of the data at least in part via avirtual machine representation of at least a portion of an operatingsystem of an end user's real machine having one or more end-userspecified preferences. Continuing the example above, FIG. 1A shows theEffect of data acceptability determination engine 106. Effect of dataacceptability determination engine 106 may receive data from dataretriever engine 102. FIG. 1A further illustrates the Effect of dataacceptability determination engine 106 further including a virtualmachine module 118 and a user preference database 120. Effect of dataacceptability determination engine 106 may transfer the data to thevirtual machine module 118. Virtual machine module 118 (FIG. 1A) mayspawn at least one virtual machine 11, 12, and/or 13, and transfer thedata to at least one of virtual machines 11, 12, and/or 13. FIG. 1Bshows virtual machines 11, 12, and 13 encompassing a virtual machinerepresentation of real machine 130, post (e.g., subsequent to)activation of Link 1, Link 2, and Link 3, respectively (e.g. as at leasta part of real machine 130 would exist had link 1, link 2, and/or link 3actually been traversed on real machine 130). FIG. 1B illustratesvirtual machine 11 including a virtual machine representation of anoperating system (e.g., a state of an operating system and/or networkoperating system) of the real machine 130 post activation of Link 1.Examples of such an operating system might include a computer operatingsystem (e.g., Microsoft® Windows 2000. Unix, Linux, etc) and/or anetwork operating system (e.g., the Internet Operating System availablefrom Cisco Technology, Inc. Netware® available from Novell, Inc., and/orSolaris available from Sun Microsystems, Inc.). An example ofdetermining an acceptability of an effect of the data at least in partvia a virtual machine representation includes determining anacceptability of an effect of the data at least in part via a virtualmachine representation of at least a portion of an operating system ofthe real machine include determining whether a portion of the operatingsystem (e.g., Microsoft Vista) on for example, virtual machine 12 hasbeen disabled after loading at least a portion of the data contained inLink 2.

The operation 2006 illustrates determining an acceptability of an effectof the retrieved at least a portion of the data at least in part via avirtual machine representation of at least a portion of an end user'sreal machine having one or more end-user specified preferences includingat least a portion of a computing device. FIG. 1D illustrates realmachine 130 including at least a part of a computing device 132. Thecomputing device 132 may be any device capable of processing one or moreprogramming instructions. For example, the computing device 132 may be adesktop computer, a laptop computer, a notebook computer, a mobilephone, a personal digital assistant (PDA), combinations thereof, and/orother suitable computing devices.

FIG. 21 illustrates alternative embodiments of the example operationalflow 200 of FIG. 2. FIG. 21 illustrates example embodiments where theoperation 220 may include at least one additional operation. Additionaloperations may include an operation 2102, and/or an operation 2104.

The operation 2102 illustrates determining an acceptability of an effectof the retrieved at least a portion of the data at least in part via avirtual machine representation of at least a portion of an end user'sreal machine having one or more end-user specified preferences includingat least one peripheral device. Continuing the example above, FIG. 1Ashows the Effect of data acceptability determination engine 106. Effectof data acceptability determination engine 106 may receive data fromdata retriever engine 102. FIG. 1A further illustrates the Effect ofdata acceptability determination engine 106 further including a virtualmachine module 118 and a user preference database 120. Effect of dataacceptability determination engine 106 may transfer the data to thevirtual machine module 118. Virtual machine module 118 (FIG. 1A) mayspawn at least one virtual machine 11, 12, and/or 13 that may be avirtual machine representation of at least a part of real machine 130.Real machine 130 (FIG. 1B) may include at least one peripheral device.For instance, FIG. 1D illustrates real machine 130 including at leastone peripheral device 134-146. FIG. 1D shows a representative view of animplementation of real machine 130 (e.g., a desktop, notebook, or othertype computing system, and/or one or more peripheral devices) in whichall/part of system 100 may be implemented. FIG. 1D illustrates thatimplementations of real machine 130 may include all/part of computingdevice 132 and/or all/part of one or one or more peripherals associatedcomputing device 132.

The operation 2104 illustrates determining an acceptability of an effectof the retrieved at least a portion of the data at least in part via avirtual machine representation of at least a portion of an end user'sreal machine having one or more end-user specified preferences includingat least one peripheral device including at least one peripheral devicethat is at least one of a printer, a fax machine, a peripheral memorydevice, a network adapter, a music player, a cellular telephone, a dataacquisition device, or a device actuator. Continuing the example above,FIG. 1A shows the Effect of data acceptability determination engine 106.Effect of data acceptability determination engine 106 may receive datafrom data retriever engine 102. FIG. 1A further illustrates the Effectof data acceptability determination engine 106 further including avirtual machine module 118 and a user preference database 120. Effect ofdata acceptability determination engine 106 may transfer the data to thevirtual machine module 118. Virtual machine module 118 (FIG. 1A) mayspawn at least one of virtual machines 11, 12, and/or 13 that may be avirtual machine representation of at least a part of real machine 130.Real machine 130 may include at least one peripheral device. Forinstance, FIG. 1D illustrates an end user's real machine may alsoinclude at least a portion of one or more peripheral devicesconnected/connectable (e.g., via wired, waveguide, or wirelessconnections) to real machine 130. Peripheral devices may include one ormore printers 134, one or more fax machines 136, one or more peripheralmemory devices 138 (e.g., flash drive, memory stick), one or morenetwork adapters 139 (e.g., wired or wireless network adapters), one ormore music players 140, one or more cellular telephones 142, one or moredata acquisition devices 144 (e.g., robots) and/or one or more deviceactuators 146 (e.g., a computer-controlled manufacturing device, medicaldevice, an hydraulic arm, a radiation emitter, or any other component(s)of industrial/medical systems).

FIG. 22 illustrates alternative embodiments of the example operationalflow 200 of FIG. 2. FIG. 22 illustrates example embodiments where theoperation 230 may include at least one additional operation. Additionaloperations may include an operation 2202, and/or an operation 2204.

The operation 2202 illustrates providing a data display option ofdisplaying the retrieved at least a portion of the data. Continuing theexample above, data provider engine 108 (FIG. 1A) may be incommunication with Effect of data acceptability determination engine 106(FIG. 1A), which may receive data from data obtainer engine 102 (FIG.1A). Effect of data acceptability determination engine 106 may transfereffect of data acceptability determination to the data provider engine108 to provide the data display option of displaying at least a portionof the data. For instance, data provider engine 108 may receive at leastone display instruction (e.g., OK to display the entire text of link 1)from at least one component of Effect of data acceptabilitydetermination engine 106 (FIG. 1A). At least one of virtual machines 11,12, and/or 13 may include one or more instruction generating modulesconfigured to provide an instruction to the Effect of data acceptabilitydetermination engine 106 after a comparison of an activation of a linkto a user preference stored in a copy of the user preference database120 (FIG. 1A) spawned on the virtual machine 11, 12, and/or 13. Effectof data acceptability determination engine 106 may communicate thedisplay instruction to the data provider engine 108. Data providerengine 108 may then display the data. Displayed data may be anunmodified web page of text, images and/or video, or a web pageincluding links to additional web pages and may be displayed on an enduser's real machine display such as a computer screen.

The operation 2204 illustrates providing a data display option of notdisplaying the retrieved at least a portion of the data. Continuing theexample above, data provider engine 108 (FIG. 1A) may be incommunication with Effect of data acceptability determination engine 106(FIG. 1A), which may receive data from data obtainer engine 102 (FIG.1A). Effect of data acceptability determination engine 106 may transfereffect of data acceptability determination to the data provider engine108 to provide the data display option of not displaying at least aportion of the data. For instance, data provider engine 108 may receiveat least one do not display instruction (e.g., Do not display the textof link 1) from at least one component of Effect of data acceptabilitydetermination engine 106 (FIG. 1A). At least one of virtual machines 11,12, and/or 13 (FIG. 1B) may include one or more instruction generatingmodules configured to provide a do not display instruction to the Effectof data acceptability determination engine 106 after a comparison of anactivation of a link to a user preference stored in a copy of the userpreference database 120 (FIG. 1A) spawned on the virtual machine 11, 12,and/or 13. Effect of data acceptability determination engine 106 maycommunicate the do not display instruction to the data provider engine108. The data display option of not displaying the data may include amessage indicated why the data is not being displayed, or may be, forexample, a blank page displayed on a display of the real machine.

FIG. 23 illustrates alternative embodiments of the example operationalflow 200 of FIG. 2. FIG. 23 illustrates example embodiments where theoperation 230 may include at least one additional operation. Additionaloperations may include an operation 2302, an operation 2304, and/or anoperation 2306.

The operation 2302 illustrates providing a data display option ofdisplaying a modified version of the retrieved at least a portion of thedata. Continuing the example above, data provider engine 108 (FIG. 1A)may be in communication with Effect of data acceptability determinationengine 106 (FIG. 1A), which may receive data from data obtainer engine102 (FIG. 1A). Effect of data acceptability determination engine 106 maytransfer effect of data acceptability determination to the data providerengine 108 to provide the data display option of displaying at least amodified version of the data. For instance, data provider engine 108(FIG. 1A) may receive at least one modify data instruction (e.g.,display only lines 1-10 of the text of link 1) from at least onecomponent of Effect of data acceptability determination engine 106 (FIG.1A). At least one of virtual machines 11, 12, and/or 13 may include oneor more instruction generating modules configured to provide a modifydata instruction to the Effect of data acceptability determinationengine 106 after a comparison of an activation of a link to a userpreference stored in a copy of the user preference database 120 (FIG.1A) spawned on the virtual machine 11, 12, and/or 13. Effect of dataacceptability determination engine 106 may communicate the modify datainstruction to the data provider engine 108. The data provider engine108 may transmit the modify data instruction to the data modificationengine 122 for modification of the data. Data modification engine maytransmit the modified data to the data provider engine 108. Dataprovider engine 108 may then display the modified version of the data.Displayed data may be a modified web page of text, a modified imageand/or a modified video, or a modified web page including links toadditional web pages. For instance, a webpage or website may bedisplaying, but any obscenities on the web page or website may replacedby non-obscene word alternatives.

The operation 2304 illustrates providing a data display option ofobfuscating an objectionable data portion. Continuing the example above,data provider engine 108 (FIG. 1A) may be in communication with Effectof data acceptability determination engine 106 (FIG. 1A), which mayreceive data from data obtainer engine 102 (FIG. 1A). Effect of dataacceptability determination engine 106 may transfer effect of dataacceptability determination to the data provider engine 108 to providethe data display option of obfuscating (e.g., blurring) a portion of thedata (e.g., obscene photos). For instance, data provider engine 108 mayreceive at least one obfuscate data instruction (e.g., display onlynon-obscene portions of the image in link 1) from at least one componentof Effect of data acceptability determination engine 106 (FIG. 1A). Atleast one of virtual machines 11, 12, and/or 13 (FIG. 1B) may includeone or more instruction generating modules configured to provide anobfuscate data instruction to the Effect of data acceptabilitydetermination engine 106 after a comparison of an activation of a linkto a user preference stored in a copy of the user preference database120 (FIG. 1A) spawned on the virtual machine 11, 12, and/or 13. Effectof data acceptability determination engine 106 may communicate theobfuscate data instruction to the data provider engine 108. The dataprovider engine 108 may transmit the obfuscate data instruction to thedata modification engine 122 which may transmit the obfuscate datainstruction to the data obfuscation engine 124. Data obfuscation engine124 may transmit the obfuscated data to the data modification engine 122for transmission to the data provider engine 108. Data provider engine108 may then display the obfuscated version of the data. For example,obfuscating logic may obfuscate restricted data or imagery within awebpage or image. Obfuscation may include blurring or blocking of theobjectionable data portion.

The operation 2306 illustrates providing a data display option ofanonymizing an objectionable data portion. Continuing the example above,data provider engine 108 (FIG. 1A) may be in communication with Effectof data acceptability determination engine 106 (FIG. 1A), which mayreceive data from data obtainer engine 102 (FIG. 1A). Effect of dataacceptability determination engine 106 may transfer effect of dataacceptability determination and an instruction to the data providerengine 108 to provide the data display option of anonymizing (e.g.,obscuring source information) for a portion of the data (e.g., graphicvideos). For instance, data provider engine 108 may receive at least oneanonymize data instruction (e.g., obscure source information forportions of the video in link 1) from at least one component of Effectof data acceptability determination engine 106 (FIG. 1A). At least oneof virtual machines 11, 12, and/or 13 may include one or moreinstruction generating modules configured to provide an anonymize datainstruction to the Effect of data acceptability determination engine 106after a comparison of an activation of a link to a user preferencestored in a copy of the user preference database 120 (FIG. 1A) spawnedon the virtual machine 11, 12, and/or 13. Effect of data acceptabilitydetermination engine 106 may communicate the anonymize data instructionto the data provider engine 108. The data provider engine 108 maytransmit the anonymize data instruction to the data modification engine122 which may transmit the anonymize data instruction to the dataanonymization engine 126. Data anonymization engine 126 may transmit theanonymized data to the data modification engine 122 for transmission tothe data provider engine 108. Data provider engine 108 may then displaythe anonymized version of the data. Anonymized data may be data in whichthe original identity information of the data is hidden, obscured,replaced, and/or otherwise modified.

FIG. 24 illustrates alternative embodiments of the example operationalflow 200 of FIG. 2. FIG. 24 illustrates example embodiments where theoperation 230 may include at least one additional operation. Additionaloperations may include an operation 2402, an operation 2404, and/or anoperation 2406. The operation 2402 illustrates providing a data displayoption of removing, altering, or replacing an objectionable dataportion. Continuing the example above, data provider engine 108 (FIG.1A) may be in communication with Effect of data acceptabilitydetermination engine 106 (FIG. 1A), which may receive data from dataobtainer engine 102 (FIG. 1A). Effect of data acceptabilitydetermination engine 106 may transfer effect of data acceptabilitydetermination and an instruction to the data provider engine 108 toprovide the data display option of removing, altering or replacing anobjectionable data portion (e.g., replacing profanity with innocuouslanguage) for a portion of the data (e.g., explicit lyrics). Forinstance, data provider engine 108 may receive at least one alter,remove or replace instruction (e.g., obscure source information forportions of the video in link 1) from at least one component of Effectof data acceptability determination engine 106 (FIG. 1A). At least oneof virtual machines 11, 12, and/or 13 may include one or moreinstruction generating modules configured to provide a remove, alter orreplace data instruction to the Effect of data acceptabilitydetermination engine 106 after a comparison of an activation of a linkto a user preference stored in a copy of the user preference database120 (FIG. 1A) spawned on the virtual machine 11, 12, and/or 13. Effectof data acceptability determination engine 106 may communicate theremove, alter or replace data instruction to the data provider engine108. The data provider engine 108 may transmit the anonymize datainstruction to the data modification engine 122 which may then remove,alter or replace the data. Data modification engine 122 may transmit thedata containing removed, altered or replaced portions to the dataprovider engine 108. Data provider engine 108 may then display the datacontaining removed, altered, or replaced portions. Thus, in one specificexample, a portion of a webpage produced by a search including datarelating to religions other than Catholicism may be removed from the webpage prior to display of the data on an end user's real machine displaysuch as a computer screen.

The operation 2404 illustrates providing a data display option ofdisplaying a data portion consistent with at least one setting.Continuing the example above, data provider engine 108 (FIG. 1A) may bein communication with Effect of data acceptability determination engine106 (FIG. 1A), which may receive data from data obtainer engine 102(FIG. 1A). Effect of data acceptability determination engine 106 maytransfer effect of data acceptability determination to the data providerengine 108 to provide the data display option of displaying dataconsistent with at least one setting. For instance, data provider engine108 may receive at least one display instruction (e.g., OK to displaywebpage) from at least one component of Effect of data acceptabilitydetermination engine 106 (FIG. 1A). At least one of virtual machines 11,12, and/or 13 may include one or more instruction generating modulesconfigured to provide an instruction to the Effect of data acceptabilitydetermination engine 106 after a comparison of an activation of a linkto a setting stored, for example, in a copy of the user preferencedatabase 120 (FIG. 1A) spawned on the virtual machine 11, 12, and/or 13.Effect of data acceptability determination engine 106 may communicatethe display instruction to the data provider engine 108. If displayeddata needs to be modified to be consistent with at least one setting,the data provider engine 108 (FIG. 1A) may transmit the modify datainstruction to the data modification engine 122 for modification of thedata. Data modification engine 122 (FIG. 1A) may transmit the modifieddata to the data provider engine 108. Data provider engine 108 may thendisplay the data consistent with the setting. Thus, a webpage or websitedata may be determined to be displayable if the data satisfies a settingwhen at least one of virtual machines 11, 12, and/or 13 compares thedata to the setting. For instance, a portion of a webpage produced by asearch including non-English text may be removed from the web page priorto display of the data on a computer screen. Further, in one specificexample, a webpage or website data may be determined to be displayableif the data satisfies a peer setting, or a webpage or website data maybe determined to be displayable if the data satisfies, for instance, acorporate setting.

The operation 2406 illustrates providing a data display option ofdisplaying a data portion consistent with a privacy related setting.Continuing the example above, at least one of virtual machines 11, 12,and/or 13 may include one or more instruction generating modulesconfigured to provide an instruction to the Effect of data acceptabilitydetermination engine 106 after a comparison of an activation of a linkto a privacy related setting stored in a copy of the user preferencedatabase 120 (FIG. 1A) spawned on the virtual machine 11, 12, and/or 13.Effect of data acceptability determination engine 106 may communicatethe display instruction to the data provider engine 108. If displayeddata needs to be modified to be consistent with at least one privacyrelated setting, the data provider engine 108 (FIG. 1A) may transmit themodify data instruction to the data modification engine 122 (FIG. 1A)for modification of the data. Data modification engine 122 may transmitthe modified data to the data provider engine 108. Data provider engine108 may then display the data consistent with the privacy relatedsetting. For instance, a portion of a returned webpage including datarequesting private user information such as a user's social securitynumber or e-mail address may be removed from the web page prior todisplay of the data on a computer screen. Further specific examplesinclude a webpage or website data may be determined to be displayable ifthe data satisfies a setting such as a privacy related setting such as asetting relating to a user's biographical information or financialinformation, a webpage or website data may be determined to bedisplayable if the data satisfies a group privacy related setting suchas a work group (e.g., employees of a company), a peer group (e.g.,members of a book club), or a family group (e.g., members of familyunit) privacy related setting, or a webpage or website data may bedetermined to be displayable if the data satisfies a privacy settingdetermined by a corporation or other organization to maintain corporateor organization privacy.

FIG. 25 illustrates alternative embodiments of the example operationalflow 200 of FIG. 2. FIG. 25 illustrates example embodiments where theoperation 230 may include at least one additional operation. Additionaloperations may include an operation 2502. The operation 2502 illustratesproviding a data display option of displaying a data portion consistentwith a user setting. Continuing the example above, Effect of dataacceptability determination engine 106 may transfer effect of dataacceptability determination to the data provider engine 108 to providethe data display option of displaying data consistent with at least oneuser setting. For instance, data provider engine 108 may receive atleast one display instruction (e.g., OK to display webpage) from atleast one component of Effect of data acceptability determination engine106 (FIG. 1A). At least one of virtual machines 11, 12, and/or 13 mayinclude one or more instruction generating modules configured to providean instruction to the Effect of data acceptability determination engine106 after a comparison of an activation of a link to a user settingstored in a copy of the user preference database 120 (FIG. 1A) spawnedon the virtual machine 11, 12, and/or 13. Effect of data acceptabilitydetermination engine 106 may communicate the display instruction to thedata provider engine 108. If displayed data needs to be modified to beconsistent with at least one user setting, the data provider engine 108(FIG. 1A) may transmit the modify data instruction to the datamodification engine 122 for modification of the data. Data modificationengine 122 (FIG. 1A) may transmit the modified data to the data providerengine 108. Data provider engine 108 may then display the dataconsistent with the user setting. Thus, a webpage or website data may bedetermined to be displayable if the data satisfies a user setting whenat least one of virtual machines 11, 12, and/or 13 compares the data tothe user setting. For instance, a portion of a webpage produced by asearch including non-English text may be removed from the web page priorto display of the data on a computer screen. Further, in one specificexample, a webpage or website data may be determined to be displayableif the data satisfies a peer user setting, or a webpage or website datamay be determined to be displayable if the data satisfies, for instance,a corporate user setting.

FIG. 26 illustrates alternative embodiments of the example operationalflow 200 of FIG. 2. FIG. 26 illustrates example embodiments where theoperation 230 may include at least one additional operation. Additionaloperations may include an operation 2602. The operation 2602 illustratesproviding a data display option of displaying a data portion consistentwith a desirability setting. Continuing the example above, at least oneof virtual machines 11, 12, and/or 13 may include one or moreinstruction generating modules configured to provide an instruction tothe Effect of data acceptability determination engine 106 after acomparison of an activation of a link to a desirability setting storedin a copy of the user preference database 120 (FIG. 1A) spawned on thevirtual machine 11, 12, and/or 13. Effect of data acceptabilitydetermination engine 106 may communicate the display instruction to thedata provider engine 108. If displayed data needs to be modified to beconsistent with at least one desirability setting, the data providerengine 108 (FIG. 1A) may transmit the modify data instruction to thedata modification engine 122 (FIG. 1A) for modification of the data.Data modification engine 122 may transmit the modified data to the dataprovider engine 108. Data provider engine 108 may then display the dataportion consistent with the desirability setting. For instance, the datadisplay option may be displaying on a display of an end user's realmachine only a data portion consistent with a Christian desirabilitysetting such as “display only Christianity related data.” In otherexamples, a webpage or website data may be determined to be displayableif the data satisfies a desirability setting, a webpage or website datamay be determined to be displayable if the data satisfies a religiousdesirability setting such as a Christian, Jewish, and/or Muslim, basedreligious desirability setting, or may be based on any other major,minor or alternative religious desirability setting, a webpage orwebsite data may be determined to be displayable if the data satisfies apolitical desirability setting such as a Republican, Democratic,Libertarian or Green Party political desirability setting, a webpage orwebsite data may be determined to be displayable if the data satisfies acultural desirability setting such as a religious, ethnic, regional, orheritage based cultural desirability setting or any other culturaldesirability setting, a webpage or website data may be determined to bedisplayable if the data satisfies a theme related desirability settingsuch as boating or card games, or a webpage or website data may bedetermined to be displayable if the data satisfies an ageappropriateness desirability setting such as a setting based on theMotion Picture of America Association film rating system.

FIG. 27 illustrates alternative embodiments of the example operationalflow 200 of FIG. 2. FIG. 27 illustrates example embodiments where theoperation 230 may include at least one additional operation. Additionaloperations may include an operation 2702. The operation 2702 illustratesproviding a data display option of displaying a data portion consistentwith a workplace established setting. Continuing the example above, atleast one of virtual machines 11, 12, and/or 13 may include one or moreinstruction generating modules configured to provide an instruction tothe Effect of data acceptability determination engine 106 after acomparison of an activation of a link to a workplace established settingstored in a copy of the user preference database 120 (FIG. 1A) spawnedon the virtual machine 11, 12, and/or 13. Effect of data acceptabilitydetermination engine 106 may communicate the display instruction to thedata provider engine 108. If displayed data needs to be modified to beconsistent with at least one workplace established setting, the dataprovider engine 108 (FIG. 1A) may transmit the modify data instructionto the data modification engine 122 (FIG. 1A) for modification of thedata. Data modification engine 122 may transmit the modified data to thedata provider engine 108. Data provider engine 108 may then display thedata portion consistent with the workplace established setting. Forinstance, the data display option may be displaying on a display of anend user's real machine only a data portion consistent with a workplaceappropriateness desirability setting such as “display only non-obscenedata.”

FIG. 28 illustrates alternative embodiments of the example operationalflow 200 of FIG. 2. FIG. 28 illustrates example embodiments where theoperation 230 may include at least one additional operation. Additionaloperations may include an operation 2802, and/or an operation 2804. Theoperation 2802 illustrates providing a data display option of displayinga data portion consistent with a safety setting. Continuing the exampleabove, at least one of virtual machines 11, 12, and/or 13 may includeone or more instruction generating modules configured to provide aninstruction to the Effect of data acceptability determination engine 106after a comparison of an activation of a link to a safety setting storedin a copy of the user preference database 120 (FIG. 1A) spawned on thevirtual machine 11, 12, and/or 13. Effect of data acceptabilitydetermination engine 106 may communicate the display instruction to thedata provider engine 108. If displayed data needs to be modified to beconsistent with at least one safety setting, the data provider engine108 (FIG. 1A) may transmit the modify data instruction to the datamodification engine 122 (FIG. 1A) for modification of the data. Datamodification engine 122 may transmit the modified data to the dataprovider engine 108. Data provider engine 108 may then display the dataportion consistent with the safety setting. For instance, the datadisplay option may be displaying on a display of an end user's realmachine only a data portion consistent with child safety setting such as“display only non-violent data,” or “display only ethnic and genderneutral data.”

The operation 2804 illustrates providing a data display option ofdisplaying a data portion consistent with a public safety setting.Continuing the example above, at least one of virtual machines 11, 12,and/or 13 may include one or more instruction generating modulesconfigured to provide an instruction to the Effect of data acceptabilitydetermination engine 106 after a comparison of an activation of a linkto a desirability setting stored in a copy of the user preferencedatabase 120 (FIG. 1A) spawned on the virtual machine 11, 12, and/or 13.Effect of data acceptability determination engine 106 may communicatethe display instruction to the data provider engine 108. If displayeddata needs to be modified to be consistent with at least one publicsafety setting, the data provider engine 108 (FIG. 1A) may transmit themodify data instruction to the data modification engine 122 (FIG. 1A)for modification of the data. Data provider engine 108 may then displaythe data portion consistent with the public safety setting. Forinstance, the data display option may be displaying on a display of anend user's real machine only a data portion consistent with publicsafety setting such as “display only non-confidential data.”

FIG. 29 illustrates alternative embodiments of the example operationalflow 200 of FIG. 2. FIG. 29 illustrates example embodiments where theoperation 230 may include at least one additional operation. Additionaloperations may include an operation 2902. The operation 2902 illustratesproviding a data display option of displaying a data portion consistentwith a home safety setting. Continuing the example above, Effect of dataacceptability determination engine 106 may transfer effect of dataacceptability determination to the data provider engine 108 to providethe data display option of displaying data consistent with at least onehome safety setting. For instance, data provider engine 108 may receiveat least one display instruction from at least one component of Effectof data acceptability determination engine 106 (FIG. 1A). At least oneof virtual machines 11, 12, and/or 13 may include one or moreinstruction generating modules configured to provide an instruction tothe Effect of data acceptability determination engine 106 after acomparison of an activation of a link to a home safety setting stored ina copy of the user preference database 120 (FIG. 1A) spawned on thevirtual machine 1, 12, and/or 13. Effect of data acceptabilitydetermination engine 106 may communicate the display instruction to thedata provider engine 108. If displayed data needs to be modified to beconsistent with at least one home safety setting, the data providerengine 108 (FIG. 1A) may transmit the modify data instruction to thedata modification engine 122 (FIG. 1A) for modification of the data.Data provider engine 108 may then display the data portion consistentwith the home safety setting. For instance, the data display option maybe displaying on a display of an end user's real machine only a dataportion consistent with home safety setting such as “okay to displayprivate or confidential data.”

FIG. 30 illustrates alternative embodiments of the example operationalflow 200 of FIG. 2. FIG. 30 illustrates example embodiments where theoperation 230 may include at least one additional operation. Additionaloperations may include an operation 3002. The operation 3002 illustratesproviding a data display option of displaying a data portion consistentwith a workplace safety setting. Continuing the example above, Effect ofdata acceptability determination engine 106 may transfer effect of dataacceptability determination to the data provider engine 108 to providethe data display option of displaying data consistent with at least oneworkplace safety setting. For instance, data provider engine 108 mayreceive at least one display instruction from at least one component ofEffect of data acceptability determination engine 106 (FIG. 1A). Each ofvirtual machines 11, 12, and/or 13 may include one or more instructiongenerating modules configured to provide an instruction to the Effect ofdata acceptability determination engine 106 after a comparison of anactivation of a link to a workplace safety setting stored in a copy ofthe user preference database 120 (FIG. 1A) spawned on the virtualmachine 11, 12, and/or 13. Effect of data acceptability determinationengine 106 may communicate the display instruction to the data providerengine 108. If displayed data needs to be modified to be consistent withat least one workplace safety setting, the data provider engine 108(FIG. 1A) may transmit the modify data instruction to the datamodification engine 122 (FIG. 1A) for modification of the data. Dataprovider engine 108 may then display the data portion consistent withthe workplace safety setting. For instance, the data display option maybe displaying on a display of an end user's real machine only a dataportion consistent with a workplace safety setting such as “display onlynon-personal data.”

FIG. 31 illustrates alternative embodiments of the example operationalflow 200 of FIG. 2. FIG. 31 illustrates example embodiments where theoperation 230 may include at least one additional operation. Additionaloperations may include an operation 3102. The operation 3102 illustratesproviding a data display option of displaying a data portion consistentwith a child safety setting. Continuing the example above, Effect ofdata acceptability determination engine 106 may transfer effect of dataacceptability determination to the data provider engine 108 to providethe data display option of displaying data consistent with at least onechild safety setting. For instance, data provider engine 108 may receiveat least one display instruction from at least one component of Effectof data acceptability determination engine 106 (FIG. 1A). At least oneof virtual machines 11, 12, and/or 13 may include one or moreinstruction generating modules configured to provide an instruction tothe Effect of data acceptability determination engine 106 after acomparison of an activation of a link to a child safety setting storedin a copy of the user preference database 120 (FIG. 1A) spawned on thevirtual machine 11, 12, and/or 13. Effect of data acceptabilitydetermination engine 106 may communicate the display instruction to thedata provider engine 108. If displayed data needs to be modified to beconsistent with at least one child safety setting, the data providerengine 108 (FIG. 1A) may transmit the modify data instruction to thedata modification engine 122 (FIG. 1A) for modification of the data.Data provider engine 108 may then display the data portion consistentwith the child safety setting. For instance, the data display option maybe displaying on a display of an end user's real machine only a dataportion consistent with a child safety setting such as “display onlynon-violent data.”

FIG. 32 illustrates alternative embodiments of the example operationalflow 200 of FIG. 2. FIG. 32 illustrates example embodiments where theoperation 230 may include at least one additional operation. Additionaloperations may include an operation 3202, an operation 3204, and/or anoperation 3206.

The operation 3202 illustrates redirecting to alternative data.Continuing the example above, data provider engine 108 (FIG. 1A) may bein communication with Effect of data acceptability determination engine106 (FIG. 1A), which may receive data from data retriever engine 102(FIG. 1A). Effect of data acceptability determination engine 106 maytransfer effect of data acceptability determination to the data providerengine 108, and an instruction to provide the data display option ofredirecting to alternative data (e.g., another website). For instance,data provider engine 108 may receive at least one redirect instructionfrom at least one component of Effect of data acceptabilitydetermination engine 106 (FIG. 1A). Each of virtual machines 11, 12,and/or 13 may include one or more instruction generating modulesconfigured to provide a redirect instruction to the Effect of dataacceptability determination engine 106 after a comparison of anactivation of a link to a user preference stored in a copy of the userpreference database 120 (FIG. 1A) spawned on the virtual machine 11, 12,and/or 13. Effect of data acceptability determination engine 106 maycommunicate the redirect instruction to the data provider engine 108.The data provider engine 108 may transmit the redirect data instructionto the data redirection engine 128 for redirection to alternative data.The data redirection engine 128 may transmit the redirection to the dataprovider engine 108. Data provider engine 108 may then display thealternative data.

The operation 3204 illustrates automatically redirecting to alternativedata. Continuing the example above, Effect of data acceptabilitydetermination engine 106 may transfer effect of data acceptabilitydetermination to the data provider engine 108 to provide the datadisplay option of automatically redirecting to alternative data (e.g.,another website) consistent with a user preference. For instance, dataprovider engine 108 may receive at least one automatically redirectinstruction from at least one component of Effect of data acceptabilitydetermination engine 106 (FIG. 1A). At least one of virtual machines 11,12, and/or 13 may include one or more instruction generating modulesconfigured to provide an automatically redirect instruction to theEffect of data acceptability determination engine 106 after a comparisonof an activation of a link to a user preference stored in a copy of theuser preference database 120 (FIG. 1A) spawned on the virtual machine11, 12, and/or 13. Effect of data acceptability determination engine 106may communicate an automatically redirect to alternative data consistentwith the user preference instruction to the data provider engine 108.The data provider engine 108 may transmit the redirect instruction tothe data redirection engine 128 for automatic redirection to alternativedata consistent with the user preference. The data redirection engine128 may transmit the automatic redirection to the data provider engine108. Data provider engine 108 may then automatically (e.g., prior toalerting a user) display the alternative data. For instance, an enduser's real machine 130 may be automatically redirected to an acceptableweb link, or a page of acceptable data.

The operation 3206 illustrates providing a list of selectablealternative data options. Continuing the example above, Effect of dataacceptability determination engine 106 may transfer effect of dataacceptability determination to the data provider engine 108, and aninstruction to provide the data display option of providing a list ofselectable alternative data options (e.g., a list of alternativewebsites) consistent with a user preference. For instance, data providerengine 108 may receive at least one provide selectable alternativesinstruction from at least one component of Effect of data acceptabilitydetermination engine 106 (FIG. 1A). At least one of virtual machines 11,12, and/or 13 may include one or more instruction generating modulesconfigured to transmit a provide selectable alternatives instruction tothe Effect of data acceptability determination engine 106 after acomparison of an activation of a link to a user preference stored in acopy of the user preference database 120 (FIG. 1A) spawned on thevirtual machine 11, 12, and/or 13. Effect of data acceptabilitydetermination engine 106 may communicate the provide selectablealternatives instruction to the data provider engine 108. The dataprovider engine 108 may transmit the provide selectable alternativesinstruction to the data redirection engine 128 to provide selectablealternatives consistent with the user preference. The data redirectionengine 128 may transmit the list of selectable alternatives to the dataprovider engine 108. Data provider engine 108 may then display the listof selectable alternatives. For instance, the list of selectablealternative data options may include a list of acceptable web links or aselectable list of web pages. Selectable web links and web pages mayinclude a thumbnail image of the first page of the web link or of theweb page.

FIG. 33 illustrates alternative embodiments of the example operationalflow 200 of FIG. 2. FIG. 33 illustrates example embodiments where theoperation 230 may include at least one additional operation. Additionaloperations may include an operation 3302, and/or an operation 3304. Theoperation 3302 illustrates displaying alternative data consistent with aprivacy related setting. Continuing the example above, Effect of dataacceptability determination engine 106 may transfer effect of dataacceptability determination to the data provider engine 108, and aninstruction to provide the data display option of displaying dataconsistent with at least one privacy related setting. For instance, dataprovider engine 108 may receive at least one display instruction (e.g.,OK to display webpage) from at least one component of Effect of dataacceptability determination engine 106 (FIG. 1A). At least one ofvirtual machines 11, 12, and/or 13 may include one or more instructiongenerating modules configured to provide an instruction to the Effect ofdata acceptability determination engine 106 after a comparison of anactivation of a link to a privacy related setting stored in a copy ofthe user preference database 120 (FIG. 1A) spawned on the virtualmachine 11, 12, and/or 13. Effect of data acceptability determinationengine 106 may communicate the display instruction to the data providerengine 108. If data needs to be modified to be consistent with at leastone privacy related setting, the data provider engine 108 may transmitthe modify data instruction to the data modification engine 122 formodification of the data. Data modification engine 122 may transmit themodified data to the data provider engine 108. Data provider engine 108may then display the data consistent with the privacy related setting.For instance, a portion of a returned webpage including data requestingprivate user information such as a user's social security number ore-mail address may be removed from the web page prior to display of thedata on a computer screen. Further specific examples include a webpageor website data may be determined to be displayable if the datasatisfies a setting such as a privacy related setting such as a settingrelating to a user's biographical information or financial information,a webpage or website data may be determined to be displayable if thedata satisfies a group privacy related setting such as a work group(e.g., employees of a company), a peer group (e.g., members of a bookclub), or a family group (e.g., members of family unit) privacy relatedsetting, or a webpage or website data may be determined to bedisplayable if the data satisfies a privacy setting determined by acorporation or other organization to maintain corporate or organizationprivacy.

The operation 3304 illustrates displaying alternative data consistentwith a customized user setting. Continuing the example above, Effect ofdata acceptability determination engine 106 may transfer effect of dataacceptability determination to the data provider engine 108, and aninstruction to provide the data display option of displaying dataconsistent with at least one customized user setting. For instance, dataprovider engine 108 may receive at least one display instruction (e.g.,OK to display webpage) from at least one component of Effect of dataacceptability determination engine 106 (FIG. 1A). At least one ofvirtual machines 11, 12, and/or 13 may include one or more instructiongenerating modules configured to provide an instruction to the Effect ofdata acceptability determination engine 106 after a comparison of anactivation of a link to a customized user setting stored in a copy ofthe user preference database 120 (FIG. 1A) spawned on the virtualmachine 11, 12, and/or 13. Effect of data acceptability determinationengine 106 may communicate the display instruction to the data providerengine 108. If data needs to be modified to be consistent with at leastone customized user setting, the data provider engine 108 may transmitthe modify data instruction to the data modification engine 122 formodification of the data. Data modification engine 122 may transmit themodified data to the data provider engine 108. Data provider engine 108may then display the data consistent with the customized user setting.Thus, a webpage or website data may be determined to be displayable ifthe data satisfies a customized user setting when at least one ofvirtual machines 11, 12, and/or 13 compares the data to the customizeduser setting. For instance, a portion of a webpage produced by a searchincluding non-English text may be removed from the web page prior todisplay of the data on a computer screen. Further, in one specificexample, a webpage or website data may be determined to be displayableif the data satisfies a customized peer user setting, or a webpage orwebsite data may be determined to be displayable if the data satisfies,for instance, a customized corporate user setting.

FIG. 34 illustrates alternative embodiments of the example operationalflow 200 of FIG. 2. FIG. 34 illustrates example embodiments where theoperation 230 may include at least one additional operation. Additionaloperations may include an operation 3402. The operation 3402 illustratesdisplaying alternative data consistent with a desirability setting.Continuing the example above, Effect of data acceptability determinationengine 106 may transfer effect of data acceptability determination tothe data provider engine 108, and an instruction to provide the datadisplay option of displaying data consistent with at least onedesirability setting. For instance, data provider engine 108 may receiveat least one display instruction (e.g., OK to display image) from atleast one component of Effect of data acceptability determination engine106 (FIG. 1A). At least one of virtual machines 11, 12, and/or 13 mayinclude one or more instruction generating modules configured to providean instruction to the Effect of data acceptability determination engine106 after a comparison of an activation of a link to a desirabilitysetting stored in a copy of the user preference database 120 (FIG. 1A)spawned on the virtual machine 11, 12, and/or 13. Effect of dataacceptability determination engine 106 may communicate the displayinstruction to the data provider engine 108. If data needs to bemodified to be consistent with at least one desirability setting, thedata provider engine 108 may transmit the modify data instruction to thedata modification engine 122 for modification of the data. Datamodification engine 122 may transmit the modified data to the dataprovider engine 108. Data provider engine 108 may then display the dataportion consistent with the desirability setting. For instance, the datadisplay option may be displaying on a display of an end user's realmachine only a data portion consistent with a Christian desirabilitysetting such as “display only Christianity related data.” In otherexamples, a webpage or website data may be determined to be displayableif the data satisfies a desirability setting, a webpage or website datamay be determined to be displayable if the data satisfies a religiousdesirability setting such as a Christian, Jewish, and/or Muslim, basedreligious desirability setting, or may be based on any other major,minor or alternative religious desirability setting, a webpage orwebsite data may be determined to be displayable if the data satisfies apolitical desirability setting such as a Republican, Democratic,Libertarian or Green Party political desirability setting, a webpage orwebsite data may be determined to be displayable if the data satisfies acultural desirability setting such as a religious, ethnic, regional, orheritage based cultural desirability setting or any other culturaldesirability setting, a webpage or website data may be determined to bedisplayable if the data satisfies a theme related desirability settingsuch as boating or card games, or a webpage or website data may bedetermined to be displayable if the data satisfies an ageappropriateness desirability setting such as a setting based on theMotion Picture of America Association film rating system.

FIG. 35 illustrates alternative embodiments of the example operationalflow 200 of FIG. 2. FIG. 35 illustrates example embodiments where theoperation 230 may include at least one additional operation. Additionaloperations may include an operation 3502, an operation 3504, and/or anoperation 3506. The operation 3502 illustrates displaying alternativedata consistent with a workplace established setting. Continuing theexample above, data provider engine 108 may receive at least one displayinstruction (e.g., do not display data) from at least one component ofEffect of data acceptability determination engine 106 (FIG. 1A). Atleast one of virtual machines 11, 12, and/or 13 may include one or moreinstruction generating modules configured to provide an instruction tothe Effect of data acceptability determination engine 106 after acomparison of an activation of a link to a workplace established settingstored in a copy of the user preference database 120 (FIG. 1A) spawnedon the virtual machine 11, 12, and/or 13. Effect of data acceptabilitydetermination engine 106 may communicate the display instruction to thedata provider engine 108. If data needs to be modified to be consistentwith at least one workplace established setting, the data providerengine 108 may transmit the modify data instruction to the datamodification engine 122 for modification of the data. Data modificationengine 122 may transmit the modified data to the data provider engine108. Data provider engine 108 may then display the data portionconsistent with the workplace established setting. For instance, thedata display option may be displaying on a display of an end user's realmachine only a data portion consistent with a workplace appropriatenessdesirability setting such as “display only non-obscene data.”

The operation 3504 illustrates displaying alternative data consistentwith a user history setting. Continuing the example above, Effect ofdata acceptability determination engine 106 may transfer effect of dataacceptability determination to the data provider engine 108, and aninstruction to provide the data display option of redirecting toalternative data consistent with a user history setting (e.g., anotherwebsite). For instance, data provider engine 108 may receive at leastone redirect instruction from at least one component of Effect of dataacceptability determination engine 106 (FIG. 1A). At least one ofvirtual machines 11, 12, and/or 13 may include one or more instructiongenerating modules configured to provide a redirect instruction to theEffect of data acceptability determination engine 106 after a comparisonof an activation of a link to a user history setting stored in a copy ofthe user preference database 120 (FIG. 1A) spawned on the virtualmachine 11, 12, and/or 13. Effect of data acceptability determinationengine 106 may communicate the redirect to alternative data consistentwith a user history setting instruction to the data provider engine 108.The data provider engine 108 may transmit the redirect data instructionto the data redirection engine 128 for redirection to alternative dataconsistent with a user history setting. The data redirection engine 128may transmit the redirection to the data provider engine 108. Dataprovider engine 108 may then display the alternative data. For instance,displayed alternative data may be consistent with a user history such ashaving viewed only music related data and pages.

The operation 3506 illustrates displaying alternative data consistentwith a safety setting. Continuing the example above, Effect of dataacceptability determination engine 106 may transfer effect of dataacceptability determination to the data provider engine 108, and aninstruction to provide the data display option of redirecting toalternative data consistent with a safety setting (e.g., anotherwebsite). For instance, data provider engine 108 may receive at leastone redirect instruction from at least one component of Effect of dataacceptability determination engine 106 (FIG. 1A). At least one ofvirtual machines 11, 12, and/or 13 may include one or more instructiongenerating modules configured to provide a redirect instruction to theEffect of data acceptability determination engine 106 after a comparisonof an activation of a link to a safety setting stored in a copy of theuser preference database 120 (FIG. 1A) spawned on the virtual machine11, 12, and/or 13. Effect of data acceptability determination engine 106may communicate the redirect to alternative data consistent with asafety setting instruction to the data provider engine 108. The dataprovider engine 108 may transmit the redirect data instruction to thedata redirection engine 128 for redirection to alternative dataconsistent with a safety setting. The data redirection engine 128 maytransmit the redirection to the data provider engine 108. Data providerengine 108 may then display the alternative data. Displaying alternativedata consistent with a safety setting may include displaying a differentwebpage including only information consistent with a safety setting suchas “do not display links requesting credit card information.”

FIG. 36 illustrates alternative embodiments of the example operationalflow 200 of FIG. 2. FIG. 36 illustrates example embodiments where theoperation 230 may include at least one additional operation. Additionaloperations may include an operation 3602, and/or an operation 3604.

The operation 3602 illustrates displaying alternative data consistentwith a workplace safety setting. Continuing the example above. Effect ofdata acceptability determination engine 106 may transfer effect of dataacceptability determination to the data provider engine 108, and aninstruction to provide the data display option of redirecting toalternative data consistent with a workplace safety setting (e.g.,another website). For instance, data provider engine 108 may receive atleast one redirect instruction from at least one component of Effect ofdata acceptability determination engine 106 (FIG. 1A). At least one ofvirtual machines 11, 12, and/or 13 may include one or more instructiongenerating modules configured to provide a redirect instruction to theEffect of data acceptability determination engine 106 after a comparisonof an activation of a link to a workplace safety setting stored in acopy of the user preference database 120 (FIG. 1A) spawned on thevirtual machine 11, 12, and/or 13. Effect of data acceptabilitydetermination engine 106 may communicate the redirect to alternativedata consistent with a workplace safety setting instruction to the dataprovider engine 108. The data provider engine 108 may transmit theredirect data instruction to the data redirection engine 128 forredirection to alternative data consistent with a workplace safetysetting. The data redirection engine 128 may transmit the redirection tothe data provider engine 108. Data provider engine 108 may then displaythe alternative data. Displaying alternative data consistent with aworkplace safety setting may include displaying a different webpageincluding only information consistent with a workplace safety settingsuch as “do not display links requesting information on this computer.”

The operation 3604 illustrates displaying alternative data consistentwith a child safety setting. Continuing the example above, Effect ofdata acceptability determination engine 106 may transfer effect of dataacceptability determination to the data provider engine 108 to providethe data display option of redirecting to alternative data consistentwith a child safety setting (e.g., another website). For instance, dataprovider engine 108 may receive at least one redirect instruction fromat least one component of Effect of data acceptability determinationengine 106 (FIG. 1A). At least one of virtual machines 11, 12, and/or 13may include one or more instruction generating modules configured toprovide a redirect instruction to the Effect of data acceptabilitydetermination engine 106 after a comparison of an activation of a linkto a child safety setting stored in a copy of the user preferencedatabase 120 (FIG. 1A) spawned on the virtual machine 11, 12, and/or 13.Effect of data acceptability determination engine 106 may communicatethe redirect to alternative data consistent with a child safety settinginstruction to the data provider engine 108. The data provider engine108 may transmit the redirect data instruction to the data redirectionengine 128 for redirection to alternative data consistent with a childsafety setting. The data redirection engine 128 may transmit theredirection to the data provider engine 108. Data provider engine 108may then display the alternative data. Displaying alternative dataconsistent with a child safety setting may include displaying adifferent webpage including only information consistent with a childsafety setting such as “do not display links containing trailers forrated ‘R’ movies.”

FIG. 37 illustrates alternative embodiments of the example operationalflow 200 of FIG. 2. FIG. 36 illustrates example embodiments where theoperation 230 may include at least one additional operation. Additionaloperations may include an operation 3702, and/or an operation 3704. Theoperation 3702 illustrates displaying alternative data consistent with apublic safety setting. Continuing the example above, at least one ofvirtual machines 11, 12, and/or 13 may include one or more instructiongenerating modules configured to provide a redirect instruction to theEffect of data acceptability determination engine 106 after a comparisonof an activation of a link to a public safety setting stored in a copyof the user preference database 120 (FIG. 1A) spawned on the virtualmachine 11, 12, and/or 13. Effect of data acceptability determinationengine 106 may communicate the redirect to alternative data consistentwith a public safety setting instruction to the data provider engine108. The data provider engine 108 may transmit the redirect datainstruction to the data redirection engine 128 for redirection toalternative data consistent with a public safety setting. The dataredirection engine 128 may transmit the redirection to the data providerengine 108. Data provider engine 108 may then display the alternativedata. Displaying alternative data consistent with a public safetysetting may include displaying a different webpage including onlyinformation consistent with a public safety setting such as “displayonly non-confidential data.” Public safety setting may include atransmittable information safety setting, a viewable information safetysetting and a receivable information safety setting. Transmittable orviewable information may be private user information, such as creditcard numbers, bank accounts, personal identification information or anyother personal user information. Receivable information may be anyinformation such as text, images, a virus, spyware, or any otherinformation that a user's real machine may be capable of receiving froman external source.

The operation 3704 illustrates displaying alternative data consistentwith a home safety setting. Continuing the example above, Effect of dataacceptability determination engine 106 may transfer effect of dataacceptability determination to the data provider engine 108, and aninstruction to provide the data display option of redirecting toalternative data consistent with a public safety setting (e.g., anotherwebsite). For instance, data provider engine 108 may receive at leastone redirect instruction from at least one component of Effect of dataacceptability determination engine 106 (FIG. 1A). Effect of dataacceptability determination engine 106 may transfer effect of dataacceptability determination to the data provider engine 108, and aninstruction to provide the data display option of redirecting toalternative data consistent with a home safety setting (e.g., anotherwebsite). For instance, data provider engine 108 may receive at leastone redirect instruction from at least one component of Effect of dataacceptability determination engine 106 (FIG. 1A). At least one ofvirtual machines 11, 12, and/or 13 may include one or more instructiongenerating modules configured to provide a redirect instruction to theEffect of data acceptability determination engine 106 after a comparisonof an activation of a link to a home safety setting stored in a copy ofthe user preference database 120 (FIG. 1A) spawned on the virtualmachine 11, 12, and/or 13. Effect of data acceptability determinationengine 106 may communicate the redirect to alternative data consistentwith a home safety setting instruction to the data provider engine 108.The data provider engine 108 may transmit the redirect data instructionto the data redirection engine 128 for redirection to alternative dataconsistent with a home safety setting. The data redirection engine 128may transmit the redirection to the data provider engine 108. Dataprovider engine 108 may then display the alternative data. Displayingalternative data consistent with a home safety setting may includedisplaying a different webpage including only information consistentwith a home safety setting such as “do not display links requestingaddress information.”

Following are a series of flowcharts depicting implementations. For easeof understanding, the flowcharts are organized such that the initialflowcharts present implementations via an example implementation andthereafter the following flowcharts present alternate implementationsand/or expansions of the initial flowchart(s) as either sub-componentoperations or additional component operations building on one or moreearlier-presented flowcharts. Those having skill in the art willappreciate that the style of presentation utilized herein (e.g.,beginning with a presentation of a flowchart(s) presenting an exampleimplementation and thereafter providing additions to and/or furtherdetails in subsequent flowcharts) generally allows for a rapid and easyunderstanding of the various process implementations. In addition, thoseskilled in the art will further appreciate that the style ofpresentation used herein also lends itself well to modular and/orobject-oriented program design paradigms.

Those having skill in the art will recognize that the state of the arthas progressed to the point where there is little distinction leftbetween hardware, software, and/or firmware implementations of aspectsof systems; the use of hardware, software, and/or firmware is generally(but not always, in that in certain contexts the choice between hardwareand software can become significant) a design choice representing costvs. efficiency tradeoffs. Those having skill in the art will appreciatethat there are various vehicles by which processes and/or systems and/orother technologies described herein can be effected (e.g., hardware,software, and/or firmware), and that the preferred vehicle will varywith the context in which the processes and/or systems and/or othertechnologies are deployed. For example, if an implementer determinesthat speed and accuracy are paramount, the implementer may opt for amainly hardware and/or firmware vehicle; alternatively, if flexibilityis paramount, the implementer may opt for a mainly softwareimplementation; or, yet again alternatively, the implementer may opt forsome combination of hardware, software, and/or firmware. Hence, thereare several possible vehicles by which the processes and/or devicesand/or other technologies described herein may be effected, none ofwhich is inherently superior to the other in that any vehicle to beutilized is a choice dependent upon the context in which the vehiclewill be deployed and the specific concerns (e.g., speed, flexibility, orpredictability) of the implementer, any of which may vary. Those skilledin the art will recognize that optical aspects of implementations willtypically employ optically-oriented hardware, software, and or firmware.

In some implementations described herein, logic and similarimplementations may include software or other control structuressuitable to operation. Electronic circuitry, for example, may manifestone or more paths of electrical current constructed and arranged toimplement various logic functions as described herein. In someimplementations, one or more media are configured to bear adevice-detectable implementation if such media hold or transmit aspecial-purpose device instruction set operable to perform as describedherein. In some variants, for example, this may manifest as an update orother modification of existing software or firmware, or of gate arraysor other programmable hardware, such as by performing a reception of ora transmission of one or more instructions in relation to one or moreoperations described herein. Alternatively or additionally, in somevariants, an implementation may include special-purpose hardware,software, firmware components, and/or general-purpose componentsexecuting or otherwise invoking special-purpose components.Specifications or other implementations may be transmitted by one ormore instances of tangible transmission media as described herein,optionally by packet transmission or otherwise by passing throughdistributed media at various times.

Alternatively or additionally, implementations may include executing aspecial-purpose instruction sequence or otherwise invoking circuitry forenabling, triggering, coordinating, requesting, or otherwise causing oneor more occurrences of any functional operations described above. Insome variants, operational or other logical descriptions herein may beexpressed directly as source code and compiled or otherwise invoked asan executable instruction sequence. In some contexts, for example, C++or other code sequences can be compiled directly or otherwiseimplemented in high-level descriptor languages (e.g., alogic-synthesizable language, a hardware description language, ahardware design simulation, and/or other such similar mode(s) ofexpression). Alternatively or additionally, some or all of the logicalexpression may be manifested as a Verilog-type hardware description orother circuitry model before physical implementation in hardware,especially for basic operations or timing-critical applications. Thoseskilled in the art will recognize how to obtain, configure, and optimizesuitable transmission or computational elements, material supplies,actuators, or other common structures in light of these teachings.

The foregoing detailed description has set forth various embodiments ofthe devices and/or processes via the use of block diagrams, flowcharts,and/or examples. Insofar as such block diagrams, flowcharts, and/orexamples contain one or more functions and/or operations, it will beunderstood by those within the art that each function and/or operationwithin such block diagrams, flowcharts, or examples can be implemented,individually and/or collectively, by a wide range of hardware, software,firmware, or virtually any combination thereof. In one embodiment,several portions of the subject matter described herein may beimplemented via Application Specific Integrated Circuits (ASICs), FieldProgrammable Gate Arrays (FPGAs), digital signal processors (DSPs), orother integrated formats. However, those skilled in the art willrecognize that some aspects of the embodiments disclosed herein, inwhole or in part, can be equivalently implemented in integratedcircuits, as one or more computer programs running on one or morecomputers (e.g., as one or more programs running on one or more computersystems), as one or more programs running on one or more processors(e.g., as one or more programs running on one or more microprocessors),as firmware, or as virtually any combination thereof, and that designingthe circuitry and/or writing the code for the software and or firmwarewould be well within the skill of one of skill in the art in light ofthis disclosure. In addition, those skilled in the art will appreciatethat the mechanisms of the subject matter described herein are capableof being distributed as a program product in a variety of forms, andthat an illustrative embodiment of the subject matter described hereinapplies regardless of the particular type of signal bearing medium usedto actually carry out the distribution. Examples of a signal bearingmedium include, but are not limited to, the following: a recordable typemedium such as a floppy disk, a hard disk drive, a Compact Disc (CD), aDigital Video Disk (DVD), a digital tape, a computer memory, etc.; and atransmission type medium such as a digital and/or an analogcommunication medium (e.g., a fiber optic cable, a waveguide, a wiredcommunications link, a wireless communication link (e.g., transmitter,receiver, transmission logic, reception logic, etc.), etc.).

In a general sense, those skilled in the art will recognize that thevarious aspects described herein which can be implemented, individuallyand/or collectively, by a wide range of hardware, software, firmware,and/or any combination thereof can be viewed as being composed ofvarious types of “electrical circuitry.” Consequently, as used herein“electrical circuitry” includes, but is not limited to, electricalcircuitry having at least one discrete electrical circuit, electricalcircuitry having at least one integrated circuit, electrical circuitryhaving at least one application specific integrated circuit, electricalcircuitry forming a general purpose computing device configured by acomputer program (e.g., a general purpose computer configured by acomputer program which at least partially carries out processes and/ordevices described herein, or a microprocessor configured by a computerprogram which at least partially carries out processes and/or devicesdescribed herein), electrical circuitry forming a memory device (e.g.,forms of memory (e.g., random access, flash, read only, etc.)), and/orelectrical circuitry forming a communications device (e.g., a modem,communications switch, optical-electrical equipment, etc.). Those havingskill in the art will recognize that the subject matter described hereinmay be implemented in an analog or digital fashion or some combinationthereof.

Those skilled in the art will recognize that at least a portion of thedevices and/or processes described herein can be integrated into a dataprocessing system. Those having skill in the art will recognize that adata processing system generally includes one or more of a system unithousing, a video display device, memory such as volatile or non-volatilememory, processors such as microprocessors or digital signal processors,computational entities such as operating systems, drivers, graphicaluser interfaces, and applications programs, one or more interactiondevices (e.g., a touch pad, a touch screen, an antenna, etc.), and/orcontrol systems including feedback loops and control motors (e.g.,feedback for sensing position and/or velocity; control motors for movingand/or adjusting components and/or quantities). A data processing systemmay be implemented utilizing suitable commercially available components,such as those typically found in data computing/communication and/ornetwork computing/communication systems.

The herein described subject matter sometimes illustrates differentcomponents contained within, or connected with, different othercomponents. It is to be understood that such depicted architectures aremerely exemplary, and that in fact many other architectures may beimplemented which achieve the same functionality. In a conceptual sense,any arrangement of components to achieve the same functionality iseffectively “associated” such that the desired functionality isachieved. Hence, any two components herein combined to achieve aparticular functionality can be seen as “associated with” each othersuch that the desired functionality is achieved, irrespective ofarchitectures or intermedial components. Likewise, any two components soassociated can also be viewed as being “operably connected”, or“operably coupled”, to each other to achieve the desired functionality,and any two components capable of being so associated can also be viewedas being “operably couplable”, to each other to achieve the desiredfunctionality. Specific examples of operably couplable include but arenot limited to physically mateable and/or physically interactingcomponents, and/or wirelessly interactable, and/or wirelesslyinteracting components, and/or logically interacting, and/or logicallyinteractable components.

In some instances, one or more components may be referred to herein as“configured to,” “configurable to,” “operable/operative to,”“adapted/adaptable,” “able to,” “conformable/conformed to,” etc. Thoseskilled in the art will recognize that “configured to” can generallyencompass active-state components and/or inactive-state componentsand/or standby-state components, unless context requires otherwise.

While particular aspects of the present subject matter described hereinhave been shown and described, it will be apparent to those skilled inthe art that, based upon the teachings herein, changes and modificationsmay be made without departing from the subject matter described hereinand its broader aspects and, therefore, the appended claims are toencompass within their scope all such changes and modifications as arewithin the true spirit and scope of the subject matter described herein.It will be understood by those within the art that, in general, termsused herein, and especially in the appended claims (e.g., bodies of theappended claims) are generally intended as “open” terms (e.g., the term“including” should be interpreted as “including but not limited to,” theterm “having” should be interpreted as “having at least,” the term“includes” should be interpreted as “includes but is not limited to,”etc.). It will be further understood by those within the art that if aspecific number of an introduced claim recitation is intended, such anintent will be explicitly recited in the claim, and in the absence ofsuch recitation no such intent is present. For example, as an aid tounderstanding, the following appended claims may contain usage of theintroductory phrases “at least one” and “one or more” to introduce claimrecitations. However, the use of such phrases should not be construed toimply that the introduction of a claim recitation by the indefinitearticles “a” or “an” limits any particular claim containing suchintroduced claim recitation to claims containing only one suchrecitation, even when the same claim includes the introductory phrases“one or more” or “at least one” and indefinite articles such as “a” or“an” (e.g., “a” and/or “an” should typically be interpreted to mean “atleast one” or “one or more”); the same holds true for the use ofdefinite articles used to introduce claim recitations. In addition, evenif a specific number of an introduced claim recitation is explicitlyrecited, those skilled in the art will recognize that such recitationshould typically be interpreted to mean at least the recited number(e.g., the bare recitation of “two recitations,” without othermodifiers, typically means at least two recitations, or two or morerecitations). Furthermore, in those instances where a conventionanalogous to “at least one of A, B, and C, etc.” is used, in generalsuch a construction is intended in the sense one having skill in the artwould understand the convention (e.g., “a system having at least one ofA, B, and C” would include but not be limited to systems that have Aalone, B alone, C alone, A and B together, A and C together, B and Ctogether, and/or A, B, and C together, etc.). In those instances where aconvention analogous to “at least one of A, B, or C, etc.” is used, ingeneral such a construction is intended in the sense one having skill inthe art would understand the convention (e.g., “a system having at leastone of A, B, or C” would include but not be limited to systems that haveA alone, B alone, C alone, A and B together, A and C together, B and Ctogether, and/or A, B, and C together, etc.). It will be furtherunderstood by those within the art that typically a disjunctive wordand/or phrase presenting two or more alternative terms, whether in thedescription, claims, or drawings, should be understood to contemplatethe possibilities of including one of the terms, either of the terms, orboth terms. For example, the phrase “A or B” will be typicallyunderstood to include the possibilities of “A” or “B” or “A and B.”

With respect to the appended claims, those skilled in the art willappreciate that recited operations therein may generally be performed inany order. Also, although various operational flows are presented in asequence(s), it should be understood that the various operations may beperformed in other orders than those which are illustrated, or may beperformed concurrently. Examples of such alternate orderings may includeoverlapping, interleaved, interrupted, reordered, incremental,preparatory, supplemental, simultaneous, reverse, or other variantorderings, unless context dictates otherwise. Furthermore, terms like“responsive to,” “related to,” or other past-tense adjectives aregenerally not intended to exclude such variants, unless context dictatesotherwise.

1-157. (canceled)
 158. A computationally-implemented method comprising:accessing data from a data source via a virtual machine representationof at least a part of a computing machine; generating an indication ofan acceptability of an effect of at least a portion of accessed data onthe computing machine at least in part via: determining at least onestate of the virtual machine representation of at least a part of acomputing machine after accessing at least a portion of the data fromthe data source; and comparing the at least one state of the virtualmachine representation of at least a part of the computing machine to atleast one preference associated with the at least a part of a computingmachine; and controlling the at least a part of a computing machine inresponse to the indication of the acceptability of an effect of at leasta portion of accessed data on the at least a part of a computingmachine.
 159. The computationally-implemented method of claim 158,wherein the accessing data from a data source via a virtual machinerepresentation of at least a part of a computing machine includes:accessing data from a data source via a virtual machine representationof at least a part of a real machine.
 160. Thecomputationally-implemented method of claim 159, wherein the virtualmachine representation of at least a part of a real machine isinstantiated in a second real machine remote to the real machine. 161.The computationally-implemented method of claim 158, wherein theaccessing data from a data source via a virtual machine representationof at least a part of a computing machine includes: accessing data froma data source via a virtual machine representation of at least a part ofa virtual machine.
 162. (canceled)
 163. (canceled)
 164. Thecomputationally-implemented method of claim 163, wherein the file-typeassociated with the second virtual machine is configured for a mobilecomputing machine.
 165. The computationally-implemented method of claim161, wherein the accessing data from a data source via a virtual machinerepresentation of at least a part of a virtual machine includes:accessing data from a data source via a virtual machine representationof at least a part of a virtual machine representation of at least apart of a real machine.
 166. The computationally-implemented method ofclaim 158, wherein the accessing data from a data source via a virtualmachine representation of at least a part of a computing machineincludes: accessing at least one of a software program, programmingcode, an audio file, a video file, a text file, or an image file from adata source via a virtual machine representation of at least a part of acomputing machine.
 167. The computationally-implemented method of claim158, wherein the accessing data from a data source via a virtual machinerepresentation of at least a part of a computing machine includes:accessing data from a data source via a virtual machine representationof at least a part of at least one of: software of the computingmachine; hardware of the computing machine; or an operating system ofthe computing machine.
 168. The computationally-implemented method ofclaim 158, wherein the accessing data from a data source via a virtualmachine representation of at least a part of a computing machineincludes: accessing data from a computing machine accessible through anetwork via a virtual machine representation of at least a part of acomputing machine.
 169. The computationally-implemented method of claim158, wherein the accessing data from a data source via a virtual machinerepresentation of at least a part of a computing machine includes:accessing data from a data source via a virtual machine representationof at least a part of a computing machine in response an activation ofat least one link.
 170. The computationally-implemented method of claim169, wherein the accessing data from a data source via a virtual machinerepresentation of at least a part of a computing machine in response anactivation of at least one link includes: accessing data from a datasource via a virtual machine representation of at least a part of acomputing machine in response to at least one user-initiated activationof at least one link.
 171. (canceled)
 172. (canceled)
 173. (canceled)174. (canceled)
 175. (canceled)
 176. The computationally-implementedmethod of claim 169, wherein the accessing data from a data source via avirtual machine representation of at least a part of a computing machinein response an activation of at least one link includes: accessing datafrom a data source via a virtual machine representation of at least apart of a computing machine in response to an activation of at least oneof a computational event or a memory access event.
 177. Thecomputationally-implemented method of claim 169, wherein the accessingdata from a data source via a virtual machine representation of at leasta part of a computing machine in response an activation of at least onelink includes: accessing data from a data source via a virtual machinerepresentation of at least a part of a computing machine in response toat least one operation of at least one software program.
 178. (canceled)179. The computationally-implemented method of claim 169, wherein theaccessing data from a data source via a virtual machine representationof at least a part of a computing machine in response an activation ofat least one link includes: accessing data from a data source via avirtual machine representation of at least a part of a computing machinein response to at least one computing machine-initiated activation of atleast one link.
 180. (canceled)
 181. (canceled)
 182. (canceled)
 183. Thecomputationally-implemented method of claim 158, wherein the generatingan indication of an acceptability of an effect of at least a portion ofaccessed data on the computing machine includes: generating anindication of an acceptability of at least one of a modification of datacontent, a modification of hardware, a modification of software, amodification of an operating system effect, a modification of memory, amodification of a processing speed, an installation of a virus, aninstallation of software, a transfer of data, or a modification of avirtual machine resulting from the at least a portion of accessed data.184. The computationally-implemented method of claim 158, wherein thecomparing the at least one state of the virtual machine representationof at least a part of the computing machine to at least one preferenceassociated with the at least a part of a computing machine includes:comparing the at least one state of the virtual machine representationof at least a part of the computing machine to at least one end-userspecified preference associated with the at least a part of a computingmachine.
 185. The computationally-implemented method of claim 184,wherein the comparing the at least one state of the virtual machinerepresentation of at least a part of the computing machine to at leastone end-user specified preference associated with the at least a part ofa computing machine includes: comparing the at least one state of thevirtual machine representation of at least a part of the computingmachine to at least one end-user preference actively specified by anend-user.
 186. The computationally-implemented method of claim 185,wherein the comparing the at least one state of the virtual machinerepresentation of at least a part of the computing machine to at leastone end-user preference actively specified by an end-user includes:comparing the at least one state of the virtual machine representationof at least a part of the computing machine to at least one end-userpreference specified according to an end-user input.
 187. Thecomputationally-implemented method of claim 184, wherein the comparingthe at least one state of the virtual machine representation of at leasta part of the computing machine to at least one end-user specifiedpreference associated with the at least a part of a computing machineincludes: comparing the at least one state of the virtual machinerepresentation of at least a part of the computing machine to at leastone end-user preference passively specified by an end-user.
 188. Thecomputationally-implemented method of claim 187, wherein the comparingthe at least one state of the virtual machine representation of at leasta part of the computing machine to at least one end-user preferencepassively specified by an end-user includes: comparing the at least onestate of the virtual machine representation of at least a part of thecomputing machine to at least one end-user preference specifiedaccording to at least one of an acceptance of one or more terms ofservice associated with the at least a part of a computing machine, anacceptance of the computing machine, or an activation of the computingmachine.
 189. The computationally-implemented method of claim 158,wherein the comparing the at least one state of the virtual machinerepresentation of at least a part of the computing machine to at leastone preference associated with the at least a part of a computingmachine-includes: comparing the at least one state of the virtualmachine representation of at least a part of the computing machine to atleast one machine-specified preference associated with the at least apart of a computing machine.
 190. The computationally-implemented methodof claim 189, wherein the comparing the at least one state of thevirtual machine representation of at least a part of the computingmachine to at least one machine-specified preference associated with theat least a part of a computing machine includes: comparing the at leastone state of the virtual machine representation of at least a part ofthe computing machine to at least one software program-specifiedpreference associated with the at least a part of a computing machine.191. The computationally-implemented method of claim 189, wherein thecomparing the at least one state of the virtual machine representationof at least a part of the computing machine to at least onemachine-specified preference associated with the at least a part of acomputing machine includes: comparing the at least one state of thevirtual machine representation of at least a part of the computingmachine to at least one virtual machine-specified preference associatedwith the at least a part of a computing machine.
 192. Thecomputationally-implemented method of claim 158, wherein the comparingthe at least one state of the virtual machine representation of at leasta part of the computing machine to at least one preference associatedwith the at least a part of a computing machine includes: comparing theat least one state of the virtual machine representation of at least apart of the computing machine to at least one preference associated withthe at least a part of a computing machine, the at least one preferenceincluding at least one of: a preference not to load at least one ofmalware, shareware, or a rootkit.
 193. (canceled)
 194. Thecomputationally-implemented method of claim 184, wherein the comparingthe at least one state of the virtual machine representation of at leasta part of the computing machine to at least one end-user specifiedpreference associated with the at least a part of a computing machineincludes: comparing the at least one state of the virtual machinerepresentation of at least a part of the computing machine to at leastone setting associated with the at least a part of a computing machine,the at least one setting including at least one of a setting a specifiedby an end-user or a setting passively specified by an end-user.
 195. Thecomputationally-implemented method of claim 194, wherein the comparingthe at least one state of the virtual machine representation of at leasta part of the computing machine to at least one setting associated withthe at least a part of a computing machine, the at least one settingincluding a setting passively specified by an end-user includes:comparing the at least one state of the virtual machine representationof at least a part of the computing machine to at least one settingspecified according to at least one of an acceptance of one or moreterms of service associated with the at least a part of a computingmachine, an acceptance of the computing machine, or an activation of thecomputing machine.
 196. (canceled)
 197. The computationally-implementedmethod of claim 194, wherein the comparing the at least one state of thevirtual machine representation of at least a part of the computingmachine to at least one setting associated with the at least a part of acomputing machine, the at least one setting including at least one of asetting a specified by an end-user or a setting passively specified byan end-user includes: comparing the at least one state of the virtualmachine representation of at least a part of the computing machine to atleast one user setting associated with the at least a part of acomputing machine.
 198. The computationally-implemented method of claim197, wherein the comparing the at least one state of the virtual machinerepresentation of at least a part of the computing machine to at leastone user setting associated with the at least a part of a computingmachine includes: comparing the at least one state of the virtualmachine representation of at least a part of the computing machine to atleast one user setting input associated with the at least a part of acomputing machine.
 199. (canceled)
 200. (canceled)
 201. (canceled) 202.(canceled)
 203. (canceled)
 204. The computationally-implemented methodof claim 158, wherein the controlling the at least a part of a computingmachine in response to the indication of the acceptability of an effectof at least a portion of accessed data on the at least a part of acomputing machine includes: controlling display of at least a portion ofthe data from the data source.
 205. The computationally-implementedmethod of claim 158, wherein the controlling the at least a part of acomputing machine in response to the indication of the acceptability ofan effect of at least a portion of accessed data on the at least a partof a computing machine includes: at least one of: removing at least aportion of the data from the data source; altering at least a portion ofthe data from the data source; or replacing at least a portion of thedata from the data source.
 206. The computationally-implemented methodof claim 158, wherein the controlling the at least a part of a computingmachine in response to the indication of the acceptability of an effectof at least a portion of accessed data on the at least a part of acomputing machine includes: at least one of: initiating processing of atleast a portion of the data from the data source, continuing processingof at least a portion of the data from the data source, suspendingprocessing of at least a portion of the data from the data source,stopping processing of at least a portion of the data from the datasource, resuming processing of at least a portion of the data from thedata source.
 207. The computationally-implemented method of claim 158,wherein the controlling the at least a part of a computing machine inresponse to the indication of the acceptability of an effect of at leasta portion of accessed data on the at least a part of a computing machineincludes: at least one of: generating an event; generating an eventrecord; generating a event notification; or updating an event log file.208. The computationally-implemented method of claim 158, wherein thecontrolling the at least a part of a computing machine in response tothe indication of the acceptability of an effect of at least a portionof accessed data on the at least a part of a computing machine includes:configuring the computing machine to correspond with at least one priorstate of the at least one virtual machine representation of at least apart of a computing machine.
 209. The computationally-implemented methodof claim 158, wherein the controlling the at least a part of a computingmachine in response to the indication of the acceptability of an effectof at least a portion of accessed data on the at least a part of acomputing machine includes: configuring the computing machine tocorrespond with at least one state of a second virtual machinerepresentation of at least a part of a computing machine.
 210. Thecomputationally-implemented method of claim 158, wherein the controllingthe at least a part of a computing machine in response to the indicationof the acceptability of an effect of at least a portion of accessed dataon the at least a part of a computing machine includes: preventing theat least a part of a computing machine from installing at least onesoftware program.
 211. The computationally-implemented method of claim158, wherein the controlling the at least a part of a computing machinein response to the indication of the acceptability of an effect of atleast a portion of accessed data on the at least a part of a computingmachine includes: preventing the at least a part of a computing machinefrom resetting at least one of: a hardware option of the computingmachine; or a software option of the computing machine.
 212. Thecomputationally-implemented method of claim 158, wherein the controllingthe at least a part of a computing machine in response to the indicationof the acceptability of an effect of at least a portion of accessed dataon the at least a part of a computing machine includes: preventing theat least a part of a computing machine from configuring at least one of:a hardware profile of the computing machine, an operating system of thecomputing machine.
 213. A non-transitory computer readable mediumincluding computer readable instructions for execution of a process on acomputing device, the process comprising: accessing data from a datasource via a virtual machine representation of at least a part of acomputing machine; generating an indication of an acceptability of aneffect of at least a portion of accessed data on the computing machineat least in part via:: determining at least one state of the virtualmachine representation of at least a part of a computing machine afteraccessing at least a portion of the data from the data source; andcomparing the at least one state of the virtual machine representationof at least a part of the computing machine to at least one preferenceassociated with the at least a part of a computing machine; andcontrolling the at least a part of a computing machine in response tothe indication of the acceptability of an effect of at least a portionof accessed data on the at least a part of a computing machine.
 214. Asystem comprising: circuitry for accessing data from a data source via avirtual machine representation of at least a part of a computingmachine; circuitry for generating an indication of an acceptability ofan effect of at least a portion of accessed data on the computingmachine at least in part via:: determining at least one state of thevirtual machine representation of at least a part of a computing machineafter accessing at least a portion of the data from the data source; andcomparing the at least one state of the virtual machine representationof at least a part of the computing machine to at least one preferenceassociated with the at least a part of a computing machine; andcircuitry for controlling the at least a part of a computing machine inresponse to the indication of the acceptability of an effect of at leasta portion of accessed data on the at least a part of a computingmachine.